ABSTRACT Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis

of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost,

minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies

and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the

which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a

single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions

can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described

for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random

variations are present. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your institution

Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 12

print issues and online access $259.00 per year only $21.58 per issue Learn more Buy this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be

subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR

CONTENT BEING VIEWED BY OTHERS CHEMOMETRIC ANALYSIS IN RAMAN SPECTROSCOPY FROM EXPERIMENTAL DESIGN TO MACHINE LEARNING–BASED MODELING Article 05 November 2021 RSPSSL: A NOVEL HIGH-FIDELITY

RAMAN SPECTRAL PREPROCESSING SCHEME TO ENHANCE BIOMEDICAL APPLICATIONS AND CHEMICAL RESOLUTION VISUALIZATION Article Open access 20 February 2024 STABILITY OF PERSON-SPECIFIC BLOOD-BASED

INFRARED MOLECULAR FINGERPRINTS OPENS UP PROSPECTS FOR HEALTH MONITORING Article Open access 08 March 2021 DATA AVAILABILITY The datasets generated and/or analyzed during the current study

are available from the corresponding authors on reasonable request. SOFTWARE AVAILABILITY Outlier detection algorithm: https://doi.org/10.6084/m9.figshare.7066613.v1 REFERENCES * Baker, M.

J. et al. Clinical applications of infrared and Raman spectroscopy: state of play and future challenges. _Analyst_ 143, 1735–1757 (2018). Article  CAS  PubMed  Google Scholar  * Melin, A.

M., Perromat, A. & Déléris, G. Pharmacologic application of Fourier transform IR spectroscopy: in vivo toxicity of carbon tetrachloride on rat liver. _Biopolymers_ 57, 160–168 (2000).

Article  CAS  PubMed  Google Scholar  * Eliasson, C. & Matousek, P. Noninvasive authentication of pharmaceutical products through packaging using spatially offset Raman spectroscopy.

_Anal. Chem._ 79, 1696–1701 (2007). Article  CAS  PubMed  Google Scholar  * Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. _Nat. Protoc._ 9,

1771–1791 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Llabjani, V. et al. Polybrominated diphenyl ether-associated alterations in cell biochemistry as determined by

attenuated total reflection Fourier-transform infrared spectroscopy: a comparison with DNA-reactive and/or endocrine-disrupting agents. _Environ. Sci. Technol._ 43, 3356–3364 (2009). Article

  CAS  PubMed  Google Scholar  * Hofmann-Wellenhof, B., Lichtenegger, H. & Collins, J. _Global Positioning System: Theory and Practice_ (Springer Science & Business Media, Vienna,

2012). * Morris, P. & Perkins, A. Diagnostic imaging. _Lancet_ 379, 1525–1533 (2012). Article  PubMed  Google Scholar  * Lee, S. S. et al. Crohn disease of the small bowel: comparison of

CT enterography, MR enterography, and small-bowel follow-through as diagnostic techniques. _Radiology_ 251, 751–761 (2009). Article  PubMed  Google Scholar  * Lagleyre, S. et al.

Reliability of high-resolution CT scan in diagnosis of otosclerosis. _Otol. Neurotol._ 30, 1152–1159 (2009). Article  PubMed  Google Scholar  * Kalita, J. & Misra, U. Comparison of CT

scan and MRI findings in the diagnosis of Japanese encephalitis. _J. Neurol. Sci._ 174, 3–8 (2000). Article  CAS  PubMed  Google Scholar  * Schrevens, L., Lorent, N., Dooms, C. &

Vansteenkiste, J. The role of PET scan in diagnosis, staging, and management of non-small cell lung cancer. _Oncologist_ 9, 633–643 (2004). Article  PubMed  Google Scholar  * Jagust, W.,

Reed, B., Mungas, D., Ellis, W. & Decarli, C. What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? _Neurology_ 69, 871–877 (2007). Article  CAS  PubMed 

Google Scholar  * Zhou, M. et al. Clinical utility of breast-specific gamma imaging for evaluating disease extent in the newly diagnosed breast cancer patient. _Am. J. Surg._ 197, 159–163

(2009). Article  PubMed  Google Scholar  * Wallace, B. A. et al. Biomedical applications of synchrotron radiation circular dichroism spectroscopy: identification of mutant proteins

associated with disease and development of a reference database for fold motifs. _Faraday Discuss._ 126, 237–243 (2004). Article  CAS  PubMed  Google Scholar  * Greenfield, N. J. Using

circular dichroism spectra to estimate protein secondary structure. _Nat. Protoc._ 1, 2876–2890 (2006). Article  CAS  PubMed  PubMed Central  Google Scholar  * Micsonai, A. et al. Accurate

secondary structure prediction and fold recognition for circular dichroism spectroscopy. _Proc. Natl. Acad. Sci. USA_ 112, E3095–E3103 (2015). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Miles, A. J. & Wallace, B. A. Circular dichroism spectroscopy of membrane proteins. _Chem. Soc. Rev._ 45, 4859–4872 (2016). Article  CAS  PubMed  Google Scholar  * Brown, J.

Q., Vishwanath, K., Palmer, G. M. & Ramanujam, N. Advances in quantitative UV–visible spectroscopy for clinical and pre-clinical application in cancer. _Curr. Opin. Biotechnol._ 20,

119–131 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Yang, P.-W. et al. Visible-absorption spectroscopy as a biomarker to predict treatment response and prognosis of

surgically resected esophageal cancer. _Sci. Rep._ 6, 33414 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * World Health Organization. _Fluorescence microscopy for disease

diagnosis and environmental monitoring_. https://apps.who.int/iris/handle/10665/119734 (2005). * Shahzad, A. et al. Diagnostic application of fluorescence spectroscopy in oncology field:

hopes and challenges. _Appl. Spectrosc. Rev._ 45, 92–99 (2010). Article  CAS  Google Scholar  * Sieroń, A. et al. The role of fluorescence diagnosis in clinical practice. _Onco Targets

Ther._ 6, 977 (2013). PubMed  PubMed Central  Google Scholar  * Shin, D., Vigneswaran, N., Gillenwater, A. & Richards-Kortum, R. Advances in fluorescence imaging techniques to detect

oral cancer and its precursors. _Future Oncol._ 6, 1143–1154 (2010). Article  PubMed  Google Scholar  * Shahzad, A. et al. Emerging applications of fluorescence spectroscopy in medical

microbiology field. _J. Transl. Med._ 7, 99 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Möller-Hartmann, W. et al. Clinical application of proton magnetic resonance

spectroscopy in the diagnosis of intracranial mass lesions. _Neuroradiology_ 44, 371–381 (2002). Article  PubMed  Google Scholar  * Gowda, G. N. et al. Metabolomics-based methods for early

disease diagnostics. _Expert Rev. Mol. Diagn._ 8, 617–633 (2008). Article  CAS  PubMed  Google Scholar  * Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The

clinical use of structural MRI in Alzheimer disease. _Nat. Rev. Neurol._ 6, 67–77 (2010). Article  PubMed  PubMed Central  Google Scholar  * Chan, A. W. et al. 1 H-NMR urinary metabolomic

profiling for diagnosis of gastric cancer. _Br. J. Cancer_ 114, 59–62 (2016). Article  CAS  PubMed  Google Scholar  * Palmnas, M. S. & Vogel, H. J. The future of NMR metabolomics in

cancer therapy: towards personalizing treatment and developing targeted drugs? _Metabolites_ 3, 373–396 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Patil, P. &

Dasgupta, B. Role of diagnostic ultrasound in the assessment of musculoskeletal diseases. _Ther. Adv. Musculoskelet. Dis._ 4, 341–355 (2012). Article  PubMed  PubMed Central  Google Scholar

  * Navani, N. et al. Lung cancer diagnosis and staging with endobronchial ultrasound-guided transbronchial needle aspiration compared with conventional approaches: an open-label, pragmatic,

randomised controlled trial. _Lancet Respir. Med._ 3, 282–289 (2015). Article  PubMed  PubMed Central  Google Scholar  * Menon, U. et al. Sensitivity and specificity of multimodal and

ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

_Lancet Oncol._ 10, 327–340 (2009). Article  PubMed  Google Scholar  * Smith-Bindman, R. et al. Endovaginal ultrasound to exclude endometrial cancer and other endometrial abnormalities.

_JAMA_ 280, 1510–1517 (1998). Article  CAS  PubMed  Google Scholar  * Gajjar, K. et al. Diagnostic segregation of human brain tumours using Fourier-transform infrared and/or Raman

spectroscopy coupled with discriminant analysis. _Anal. Methods_ 5, 89–102 (2013). Article  CAS  Google Scholar  * Bury, D. et al. Phenotyping metastatic brain tumors applying

spectrochemical analyses: segregation of different cancer types. _Anal. Lett._ 52, 575–587 (2019). Article  CAS  Google Scholar  * Hands, J. R. et al. Attenuated Total Reflection Fourier

Transform Infrared (ATR-FTIR) spectral discrimination of brain tumour severity from serum samples. _J. Biophotonics_ 7, 189–199 (2014). Article  CAS  PubMed  Google Scholar  * Hands, J. R.

et al. Brain tumour differentiation: rapid stratified serum diagnostics via attenuated total reflection Fourier-transform infrared spectroscopy. _J. Neurooncol._ 127, 463–472 (2016). Article

  PubMed  PubMed Central  Google Scholar  * Walsh, M. J., Kajdacsy-Balla, A., Holton, S. E. & Bhargava, R. Attenuated total reflectance Fourier-transform infrared spectroscopic imaging

for breast histopathology. _Vib. Spectrosc._ 60, 23–28 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lane, R. & Seo, S. S. Attenuated total reflectance fourier

transform infrared spectroscopy method to differentiate between normal and cancerous breast cells. _J. Nanosci. Nanotechnol._ 12, 7395–7400 (2012). Article  CAS  PubMed  Google Scholar  *

Backhaus, J. et al. Diagnosis of breast cancer with infrared spectroscopy from serum samples. _Vib. Spectrosc._ 52, 173–177 (2010). Article  CAS  Google Scholar  * Wang, J.-S. et al. FT-IR

spectroscopic analysis of normal and cancerous tissues of esophagus. _World J. Gastroenterol._ 9, 1897–1899 (2003). Article  PubMed  PubMed Central  Google Scholar  * Maziak, D. E. et al.

Fourier-transform infrared spectroscopic study of characteristic molecular structure in cancer cells of esophagus: an exploratory study. _Cancer Detect. Prev._ 31, 244–253 (2007). Article 

CAS  PubMed  Google Scholar  * McIntosh, L. M. et al. Infrared spectra of basal cell carcinomas are distinct from non-tumor-bearing skin components. _J. Invest. Dermatol._ 112, 951–956

(1999). Article  CAS  PubMed  Google Scholar  * McIntosh, L. M. et al. Towards non-invasive screening of skin lesions by near-infrared spectroscopy. _J. Invest. Dermatol._ 116, 175–181

(2001). Article  CAS  PubMed  Google Scholar  * Mostaço-Guidolin, L. B., Murakami, L. S., Nomizo, A. & Bachmann, L. Fourier transform infrared spectroscopy of skin cancer cells and

tissues. _Appl. Spectrosc. Rev._ 44, 438–455 (2009). Article  CAS  Google Scholar  * Mordechai, S. et al. Possible common biomarkers from FTIR microspectroscopy of cervical cancer and

melanoma. _J. Microsc._ 215, 86–91 (2004). Article  CAS  PubMed  Google Scholar  * Hammody, Z., Sahu, R. K., Mordechai, S., Cagnano, E. & Argov, S. Characterization of malignant melanoma

using vibrational spectroscopy. _Sci. World J._ 5, 173–182 (2005). Article  CAS  Google Scholar  * Kondepati, V. R., Keese, M., Mueller, R., Manegold, B. C. & Backhaus, J. Application

of near-infrared spectroscopy for the diagnosis of colorectal cancer in resected human tissue specimens. _Vib. Spectrosc._ 44, 236–242 (2007). Article  CAS  Google Scholar  * Rigas, B.,

Morgello, S., Goldman, I. S. & Wong, P. Human colorectal cancers display abnormal Fourier-transform infrared spectra. _Proc. Natl. Acad. Sci. USA_ 87, 8140–8144 (1990). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Yao, H., Shi, X. & Zhang, Y. The use of FTIR-ATR spectrometry for evaluation of surgical resection margin in colorectal cancer: a pilot study of

56 samples. _J. Spectrosc._ 2014, 4 (2014). Article  CAS  Google Scholar  * Lewis, P. D. et al. Evaluation of FTIR Spectroscopy as a diagnostic tool for lung cancer using sputum. _BMC

Cancer_ 10, 640 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Akalin, A. et al. Classification of malignant and benign tumors of the lung by infrared spectral

histopathology (SHP). _Lab. Invest._ 95, 406–421 (2015). Article  PubMed  Google Scholar  * Großerueschkamp, F. et al. Marker-free automated histopathological annotation of lung tumour

subtypes by FTIR imaging. _Analyst_ 140, 2114–2120 (2015). Article  CAS  PubMed  Google Scholar  * Owens, G. L. et al. Vibrational biospectroscopy coupled with multivariate analysis extracts

potentially diagnostic features in blood plasma/serum of ovarian cancer patients. _J. Biophotonics_ 7, 200–209 (2014). Article  CAS  PubMed  Google Scholar  * Gajjar, K. et al.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer. _Analyst_ 138,

3917–3926 (2013). Article  CAS  PubMed  Google Scholar  * Theophilou, G., Lima, K. M. G., Martin-Hirsch, P. L., Stringfellow, H. F. & Martin, F. L. ATR-FTIR spectroscopy coupled with

chemometric analysis discriminates normal, borderline and malignant ovarian tissue: classifying subtypes of human cancer. _Analyst_ 141, 585–594 (2016). Article  CAS  PubMed  Google Scholar

  * Mehrotra, R., Tyagi, G., Jangir, D. K., Dawar, R. & Gupta, N. Analysis of ovarian tumor pathology by Fourier Transform Infrared Spectroscopy. _J. Ovarian Res._ 3, 27 (2010). Article

  CAS  PubMed  PubMed Central  Google Scholar  * Paraskevaidi, M. et al. Potential of mid-infrared spectroscopy as a non-invasive diagnostic test in urine for endometrial or ovarian cancer.

_Analyst_ 143, 3156–3163 (2018). Article  CAS  PubMed  Google Scholar  * Taylor, S. E. et al. Infrared spectroscopy with multivariate analysis to interrogate endometrial tissue: a novel and

objective diagnostic approach. _Br. J. Cancer_ 104, 790–797 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Paraskevaidi, M. et al. Aluminium foil as an alternative substrate

for the spectroscopic interrogation of endometrial cancer. _J. Biophotonics_ 11, e201700372 (2018). * Gajjar, K. et al. Histology verification demonstrates that biospectroscopy analysis of

cervical cytology identifies underlying disease more accurately than conventional screening: removing the confounder of discordance. _PLoS ONE_ 9, e82416 (2014). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Walsh, M. J. et al. IR microspectroscopy: potential applications in cervical cancer screening. _Cancer Lett._ 246, 1–11 (2007). Article  CAS  PubMed  Google

Scholar  * Wood, B. R., Quinn, M. A., Burden, F. R. & McNaughton, D. An investigation into FTIR spectroscopy as a biodiagnostic tool for cervical cancer. _Biospectroscopy_ 2, 143–153

(1996). Article  CAS  Google Scholar  * Podshyvalov, A. et al. Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks. _Appl. Opt._

44, 3725–3734 (2005). Article  CAS  PubMed  Google Scholar  * Theophilou, G. et al. A biospectroscopic analysis of human prostate tissue obtained from different time periods points to a

trans-generational alteration in spectral phenotype. _Sci. Rep._ 5, 13465 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Baker, M. J. et al. Investigating FTIR based

histopathology for the diagnosis of prostate cancer. _J. Biophotonics_ 2, 104–113 (2009). Article  CAS  PubMed  Google Scholar  * Derenne, A., Gasper, R. & Goormaghtigh, E. The FTIR

spectrum of prostate cancer cells allows the classification of anticancer drugs according to their mode of action. _Analyst_ 136, 1134–1141 (2011). Article  CAS  PubMed  Google Scholar  *

Gazi, E. et al. A correlation of FTIR spectra derived from prostate cancer biopsies with Gleason grade and tumour stage. _Eur. Urol._ 50, 750–761 (2006). Article  PubMed  Google Scholar  *

Paraskevaidi, M. et al. Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood. _Proc. Natl. Acad. Sci. USA_ 114, E7929–E7938 (2017). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Carmona, P. et al. Discrimination analysis of blood plasma associated with Alzheimer’s disease using vibrational spectroscopy. _J. Alzheimers Dis._ 34,

911–920 (2013). Article  CAS  PubMed  Google Scholar  * Carmona, P., Molina, M., López-Tobar, E. & Toledano, A. Vibrational spectroscopic analysis of peripheral blood plasma of patients

with Alzheimer’s disease. _Anal. Bioanal. Chem._ 407, 7747–7756 (2015). Article  CAS  PubMed  Google Scholar  * Paraskevaidi, M. et al. Blood-based near-infrared spectroscopy for the rapid

low-cost detection of Alzheimer’s disease. _Analyst_ 143, 5959–5964 (2018). Article  CAS  PubMed  Google Scholar  * Sitole, L., Steffens, F., Krüger, T. P. J. & Meyer, D. Mid-ATR-FTIR

spectroscopic profiling of HIV/AIDS sera for novel systems diagnostics in global health. _OMICS_ 18, 513–523 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Coopman, R. et

al. Glycation in human fingernail clippings using ATR-FTIR spectrometry, a new marker for the diagnosis and monitoring of diabetes mellitus. _Clin. Biochem._ 50, 62–67 (2017). Article  CAS 

PubMed  Google Scholar  * Scott, D. A. et al. Diabetes-related molecular signatures in infrared spectra of human saliva. _Diabetol. Metab. Syndr._ 2, 48 (2010). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Varma, V. K., Kajdacsy-Balla, A., Akkina, S. K., Setty, S. & Walsh, M. J. A label-free approach by infrared spectroscopic imaging for interrogating the

biochemistry of diabetic nephropathy progression. _Kidney Int._ 89, 1153–1159 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lechowicz, L., Chrapek, M., Gaweda, J.,

Urbaniak, M. & Konieczna, I. Use of Fourier-transform infrared spectroscopy in the diagnosis of rheumatoid arthritis: a pilot study. _Mol. Biol. Rep._ 43, 1321–1326 (2016). Article  CAS

  PubMed  PubMed Central  Google Scholar  * Canvin, J. et al. Infrared spectroscopy: shedding light on synovitis in patients with rheumatoid arthritis. _Rheumatology_ 42, 76–82 (2003).

Article  CAS  PubMed  Google Scholar  * Oemrawsingh, R. M. et al. Near-infrared spectroscopy predicts cardiovascular outcome in patients with coronary artery disease. _J. Am. Coll. Cardiol._

64, 2510–2518 (2014). Article  PubMed  Google Scholar  * Wang, J. et al. Near-infrared spectroscopic characterization of human advanced atherosclerotic plaques. _J. Am. Coll. Cardiol._ 39,

1305–1313 (2002). Article  PubMed  Google Scholar  * Martin, M. et al. The effect of common anticoagulants in detection and quantification of malaria parasitemia in human red blood cells by

ATR-FTIR spectroscopy. _Analyst_ 142, 1192–1199 (2017). Article  CAS  PubMed  Google Scholar  * Khoshmanesh, A. et al. Detection and quantification of early-stage malaria parasites in

laboratory infected erythrocytes by attenuated total reflectance infrared spectroscopy and multivariate analysis. _Anal. Chem._ 86, 4379–4386 (2014). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Roy, S. et al. Simultaneous ATR-FTIR based determination of malaria parasitemia, glucose and urea in whole blood dried onto a glass slide. _Anal. Chem._ 89, 5238–5245

(2017). Article  CAS  PubMed  Google Scholar  * Markus, A. P. J. et al. New technique for diagnosis and monitoring of alcaptonuria: quantification of homogentisic acid in urine with

mid-infrared spectrometry. _Anal. Chim. Acta_ 429, 287–292 (2001). Article  CAS  Google Scholar  * Grimard, V. et al. Phosphorylation-induced conformational changes of cystic fibrosis

transmembrane conductance regulator monitored by Attenuated Total Reflection-Fourier Transform IR Spectroscopy and Fluorescence Spectroscopy. _J. Biol. Chem._ 279, 5528–5536 (2004). Article

  CAS  PubMed  Google Scholar  * Aksoy, C., Guliyev, A., Kilic, E., Uckan, D. & Severcan, F. Bone marrow mesenchymal stem cells in patients with beta thalassemia major: molecular

analysis with attenuated total reflection-Fourier transform infrared spectroscopy study as a novel method. _Stem Cells Dev._ 21, 2000–2011 (2012). Article  CAS  PubMed  Google Scholar  *

Graça, G. et al. Mid-infrared (MIR) metabolic fingerprinting of amniotic fluid: a possible avenue for early diagnosis of prenatal disorders? _Anal. Chim. Acta_ 764, 24–31 (2013). Article 

CAS  PubMed  Google Scholar  * Hasegawa, J. et al. Evaluation of placental function using near infrared spectroscopy during fetal growth restriction. _J. Perinat. Med._ 38, 29–32 (2010).

Article  PubMed  Google Scholar  * Theelen, T., Berendschot, T. T., Hoyng, C. B., Boon, C. J. & Klevering, B. J. Near-infrared reflectance imaging of neovascular age-related macular

degeneration. _Graefe’s Arch. Clin. Exp. Ophthalmol._ 247, 1625–1633 (2009). Article  Google Scholar  * Semoun, O. et al. Infrared features of classic choroidal neovascularisation in

exudative age-related macular degeneration. _Br. J. Ophthalmol._ 93, 182–185 (2009). Article  CAS  PubMed  Google Scholar  * Peters, A. S. et al. Serum-infrared spectroscopy is suitable for

diagnosis of atherosclerosis and its clinical manifestations. _Vib. Spectrosc._ 92, 20–26 (2017). Article  CAS  Google Scholar  * Afara, I. O., Prasadam, I., Arabshahi, Z., Xiao, Y. &

Oloyede, A. Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. _Sci. Rep._ 7, 11463 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Bi, X. et al.

Fourier transform infrared imaging and MR microscopy studies detect compositional and structural changes in cartilage in a rabbit model of osteoarthritis. _Anal. Bioanal. Chem._ 387,

1601–1612 (2007). Article  CAS  PubMed  Google Scholar  * David-Vaudey, E. et al. Fourier Transform Infrared Imaging of focal lesions in human osteoarthritic cartilage. _Eur. Cell. Mater._

10, 51–60 (2005). Article  CAS  PubMed  Google Scholar  * Trevisan, J., Angelov, P. P., Carmichael, P. L., Scott, A. D. & Martin, F. L. Extracting biological information with

computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives. _Analyst_ 137, 3202–3215 (2012). Article  CAS  PubMed  Google

Scholar  * Andrew Chan, K. L. & Kazarian, S. G. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) imaging of tissues and live cells. _Chem. Soc. Rev._ 45, 1850–1864

(2016). Article  CAS  PubMed  Google Scholar  * Pilling, M. & Gardner, P. Fundamental developments in infrared spectroscopic imaging for biomedical applications. _Chem. Soc. Rev._ 45,

1935–1957 (2016). Article  CAS  PubMed  Google Scholar  * Martin, F. L. et al. Distinguishing cell types or populations based on the computational analysis of their infrared spectra. _Nat.

Protoc._ 5, 1748–1760 (2010). Article  CAS  PubMed  Google Scholar  * Butler, H. J. et al. Using Raman spectroscopy to characterize biological materials. _Nat. Protoc._ 11, 664–687 (2016).

Article  CAS  PubMed  Google Scholar  * Kong, L. et al. Characterization of bacterial spore germination using phase-contrast and fluorescence microscopy, Raman spectroscopy and optical

tweezers. _Nat. Protoc._ 6, 625–639 (2011). Article  CAS  PubMed  Google Scholar  * Harmsen, S., Wall, M. A., Huang, R. & Kircher, M. F. Cancer imaging using surface-enhanced resonance

Raman scattering nanoparticles. _Nat. Protoc._ 12, 1400–1414 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Beckonert, O. et al. Metabolic profiling, metabolomic and

metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. _Nat. Protoc._ 2, 2692–2703 (2007). Article  CAS  PubMed  Google Scholar  * Felten, J. et al.

Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS). _Nat. Protoc._ 10, 217–240 (2015). Article  CAS 

PubMed  Google Scholar  * Yang, H., Yang, S., Kong, J., Dong, A. & Yu, S. Obtaining information about protein secondary structures in aqueous solution using Fourier transform IR

spectroscopy. _Nat. Protoc._ 10, 382–396 (2015). Article  CAS  PubMed  Google Scholar  * Sreedhar, H. et al. High-definition Fourier transform infrared (FT-IR) spectroscopic imaging of human

tissue sections towards improving pathology. _J. Vis. Exp_. 2015, 52332 (2015). * Varriale, A. et al. Fluorescence correlation spectroscopy assay for gliadin in food. _Anal. Chem._ 79,

4687–4689 (2007). Article  CAS  PubMed  Google Scholar  * Song, X., Li, H., Al-Qadiri, H. M. & Lin, M. Detection of herbicides in drinking water by surface-enhanced Raman spectroscopy

coupled with gold nanostructures. _J. Food Meas. Charact._ 7, 107–113 (2013). Article  Google Scholar  * Osborne, B. G. & Fearn, T. Near-infrared spectroscopy in food analysis.

_Encyclopedia Anal. Chem._ 5, 4069–4082 (2000). Google Scholar  * Qu, J.-H. et al. Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent

research advances. _Crit. Rev. Food Sci. Nutr._ 55, 1939–1954 (2015). Article  CAS  PubMed  Google Scholar  * Penido, C. A. F., Pacheco, M. T. T., Lednev, I. K. & Silveira, L. Raman

spectroscopy in forensic analysis: identification of cocaine and other illegal drugs of abuse. _J. Raman Spectrosc._ 47, 28–38 (2016). Article  CAS  Google Scholar  * Ryder, A. G.

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy. _J. Forensic Sci._ 47, 275–284 (2002). Article  CAS  PubMed  Google Scholar  *

Harrigan, G. G. et al. Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for

idiosyncratic toxicity. _Toxicol. Lett._ 146, 197–205 (2004). Article  CAS  PubMed  Google Scholar  * Choo-Smith, L.-P. et al. Investigating microbial (micro) colony heterogeneity by

vibrational spectroscopy. _Appl. Environ. Microbiol._ 67, 1461–1469 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  * Helm, D., Labischinski, H., Schallehn, G. & Naumann,

D. Classification and identification of bacteria by Fourier-transform infrared spectroscopy. _Microbiology_ 137, 69–79 (1991). CAS  Google Scholar  * Carmona, P., Monzon, M., Monleon, E.,

Badiola, J. J. & Monreal, J. In vivo detection of scrapie cases from blood by infrared spectroscopy. _J. Gen. Virol._ 86, 3425–3431 (2005). Article  CAS  PubMed  Google Scholar  * Cui,

L. et al. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling. _Anal. Chem._ 90, 5082–5089 (2018).

Article  CAS  PubMed  Google Scholar  * Lasch, P. & Naumann, D. Infrared spectroscopy in microbiology. in _Encyclopedia of Analytical Chemistry_ (eds Brown, J. & Pawlu, T.) (Arcler

Press, Oakville, ON, Canada, 2015). * Maquelin, K. et al. Identification of medically relevant microorganisms by vibrational spectroscopy. _J. Microbiol. Methods_ 51, 255–271 (2002). Article

  CAS  PubMed  Google Scholar  * Day, J. S., Edwards, H. G., Dobrowski, S. A. & Voice, A. M. The detection of drugs of abuse in fingerprints using Raman spectroscopy I: latent

fingerprints. _Spectrochim. Acta A_ 60, 563–568 (2004). Article  CAS  Google Scholar  * Macleod, N. A. & Matousek, P. Emerging non-invasive raman methods in process control and forensic

applications. _Pharm. Res._ 25, 2205–2215 (2008). Article  CAS  PubMed  Google Scholar  * Lewis, I., Daniel, N. Jr, Chaffin, N., Griffiths, P. & Tungol, M. Raman spectroscopic studies of

explosive materials: towards a fieldable explosives detector. _Spectrochim. Acta A_ 51, 1985–2000 (1995). Article  Google Scholar  * Hargreaves, M. D. & Matousek, P. Threat detection of

liquid explosive precursor mixtures by Spatially Offset Raman Spectroscopy (SORS). in _Optics and Photonics for Counterterrorism and Crime Fighting V_ (ed. Lewis, C.) Proceedings of SPIE,

Vol. 7486, 74860B (International Society for Optics and Photonics, Bellingham, WA, 2009). * Ali, E. M., Edwards, H. G., Hargreaves, M. D. & Scowen, I. J. Raman spectroscopic

investigation of cocaine hydrochloride on human nail in a forensic context. _Anal. Bioanal. Chem._ 390, 1159–1166 (2008). Article  CAS  PubMed  Google Scholar  * Vergote, G. J., Vervaet, C.,

Remon, J. P., Haemers, T. & Verpoort, F. Near-infrared FT-Raman spectroscopy as a rapid analytical tool for the determination of diltiazem hydrochloride in tablets. _Eur. J. Pharm.

Sci._ 16, 63–67 (2002). Article  CAS  PubMed  Google Scholar  * Lohr, D. et al. Non-destructive determination of carbohydrate reserves in leaves of ornamental cuttings by near-infrared

spectroscopy (NIRS) as a key indicator for quality assessments. _Biosyst. Eng._ 158, 51–63 (2017). Article  Google Scholar  * Heys, K. A., Shore, R. F., Pereira, M. G. & Martin, F. L.

Levels of organochlorine pesticides are associated with amyloid aggregation in apex avian brains. _Environ. Sci. Technol._ 51, 8672–8681 (2017). Article  CAS  PubMed  Google Scholar  *

Comino, F., Aranda, V., García-Ruiz, R. & Domínguez-Vidal, A. Infrared spectroscopy as a tool for the assessment of soil biological quality in agricultural soils under contrasting

management practices. _Ecol. Indic._ 87, 117–126 (2018). Article  CAS  Google Scholar  * Eliasson, C., Macleod, N. & Matousek, P. Noninvasive detection of concealed liquid explosives

using Raman spectroscopy. _Anal. Chem._ 79, 8185–8189 (2007). Article  CAS  PubMed  Google Scholar  * Liu, H.-B., Zhong, H., Karpowicz, N., Chen, Y. & Zhang, X.-C. Terahertz spectroscopy

and imaging for defense and security applications. _Proc. IEEE_ 95, 1514–1527 (2007). Article  CAS  Google Scholar  * Golightly, R. S., Doering, W. E. & Natan, M. J. Surface-enhanced

Raman spectroscopy and homeland security: a perfect match? _ACS Nano_ 3, 2859–2869 (2009). Article  CAS  PubMed  Google Scholar  * Wang, Y., Veltkamp, D. J. & Kowalski, B. R.

Multivariate instrument standardization. _Anal. Chem._ 63, 2750–2756 (1991). Article  CAS  Google Scholar  * Brouckaert, D., Uyttersprot, J.-S., Broeckx, W. & De Beer, T. Calibration

transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale. _Talanta_ 179, 386–392

(2018). Article  CAS  PubMed  Google Scholar  * Vasconcelos de Andrade, E. W., Medeiros de Morais, C. L., Lopes da Costa, F. S. & Gomes de Lima, K. M. A multivariate control chart

approach for calibration transfer between NIR spectrometers for simultaneous determination of rifampicin and isoniazid in pharmaceutical formulation. _Curr. Anal. Chem._ 14, 488–494 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Zamora-Rojas, E., Pérez-Marín, D., De Pedro-Sanz, E., Guerrero-Ginel, J. & Garrido-Varo, A. Handheld NIRS analysis for routine

meat quality control: database transfer from at-line instruments. _Chemom. Intellig. Lab. Syst._ 114, 30–35 (2012). Article  CAS  Google Scholar  * Panchuk, V., Kirsanov, D., Oleneva, E.,

Semenov, V. & Legin, A. Calibration transfer between different analytical methods. _Talanta_ 170, 457–463 (2017). Article  CAS  PubMed  Google Scholar  * de Morais, Cd. L. M. & de

Lima, K. M. G. Determination and analytical validation of creatinine content in serum using image analysis by multivariate transfer calibration procedures. _Anal. Methods_ 7, 6904–6910

(2015). Article  CAS  Google Scholar  * Khaydukova, M. et al. Multivariate calibration transfer between two different types of multisensor systems. _Sens. Actuators B Chem._ 246, 994–1000

(2017). Article  CAS  Google Scholar  * Barreiro, P., Herrero, D., Hernández, N., Gracia, A. & León, L. Calibration transfer between portable and laboratory NIR spectrophotometers. _Acta

Hortic._ 802, 373–378 (2008). Article  CAS  Google Scholar  * Sulub, Y., LoBrutto, R., Vivilecchia, R. & Wabuyele, B. W. Content uniformity determination of pharmaceutical tablets using

five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms. _Anal. Chim. Acta_ 611, 143–150

(2008). Article  CAS  PubMed  Google Scholar  * Zhang, L., Small, G. W. & Arnold, M. A. Multivariate calibration standardization across instruments for the determination of glucose by

Fourier transform near-infrared spectrometry. _Anal. Chem._ 75, 5905–5915 (2003). Article  CAS  PubMed  Google Scholar  * Koehler, F. W. IV, Small, G. W., Combs, R. J., Knapp, R. B. &

Kroutil, R. T. Calibration transfer algorithm for automated qualitative analysis by passive Fourier transform infrared spectrometry. _Anal. Chem._ 72, 1690–1698 (2000). Article  CAS  PubMed

  Google Scholar  * Martens, H., Høy, M., Wise, B. M., Bro, R. & Brockhoff, P. B. Pre-whitening of data by covariance-weighted pre-processing. _J. Chemom._ 17, 153–165 (2003). Article 

CAS  Google Scholar  * Feudale, R. N. et al. Transfer of multivariate calibration models: a review. _Chemom. Intellig. Lab. Syst._ 64, 181–192 (2002). Article  CAS  Google Scholar  * Woody,

N. A., Feudale, R. N., Myles, A. J. & Brown, S. D. Transfer of multivariate calibrations between four near-infrared spectrometers using orthogonal signal correction. _Anal. Chem._ 76,

2595–2600 (2004). Article  CAS  PubMed  Google Scholar  * Greensill, C., Wolfs, P., Spiegelman, C. & Walsh, K. Calibration transfer between PDA-based NIR spectrometers in the NIR

assessment of melon soluble solids content. _Appl. Spectrosc._ 55, 647–653 (2001). Article  CAS  Google Scholar  * Sjöblom, J., Svensson, O., Josefson, M., Kullberg, H. & Wold, S. An

evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra. _Chemom. Intellig. Lab. Syst._ 44, 229–244 (1998). Article  Google Scholar  * Rodrigues,

R. R. et al. Evaluation of calibration transfer methods using the ATR-FTIR technique to predict density of crude oil. _Chemom. Intellig. Lab. Syst._ 166, 7–13 (2017). Article  CAS  Google

Scholar  * Andrews, D. T. & Wentzell, P. D. Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer. _Anal. Chim. Acta_ 350,

341–352 (1997). Article  CAS  Google Scholar  * Bouveresse, E., Massart, D. & Dardenne, P. Calibration transfer across near-infrared spectrometric instruments using Shenk’s algorithm:

effects of different standardisation samples. _Anal. Chim. Acta_ 297, 405–416 (1994). Article  CAS  Google Scholar  * Shenk, J. S. & Westerhaus, M. O. Populations structuring of near

infrared spectra and modified partial least squares regression. _Crop Sci._ 31, 1548–1555 (1991). Article  CAS  Google Scholar  * Paatero, P. & Tapper, U. Positive matrix factorization:

A non-negative factor model with optimal utilization of error estimates of data values. _Environmetrics_ 5, 111–126 (1994). Article  Google Scholar  * Xie, Y. & Hopke, P. K. Calibration

transfer as a data reconstruction problem. _Anal. Chim. Acta_ 384, 193–205 (1999). Article  CAS  Google Scholar  * Goodacre, R. et al. On mass spectrometer instrument standardization and

interlaboratory calibration transfer using neural networks. _Anal. Chim. Acta_ 348, 511–532 (1997). Article  CAS  Google Scholar  * Chen, W.-R., Bin, J., Lu, H.-M., Zhang, Z.-M. & Liang,

Y.-Z. Calibration transfer via an extreme learning machine auto-encoder. _Analyst_ 141, 1973–1980 (2016). Article  CAS  PubMed  Google Scholar  * Hu, Y., Peng, S., Bi, Y. & Tang, L.

Calibration transfer based on maximum margin criterion for qualitative analysis using Fourier transform infrared spectroscopy. _Analyst_ 137, 5913–5918 (2012). Article  CAS  PubMed  Google

Scholar  * Fan, W., Liang, Y., Yuan, D. & Wang, J. Calibration model transfer for near-infrared spectra based on canonical correlation analysis. _Anal. Chim. Acta_ 623, 22–29 (2008).

Article  CAS  PubMed  Google Scholar  * Isabelle, M. et al. Multi-centre Raman spectral mapping of oesophageal cancer tissues: a study to assess system transferability. _Faraday Discuss._

187, 87–103 (2016). Article  CAS  PubMed  Google Scholar  * Wang, Z., Dean, T. & Kowalski, B. R. Additive background correction in multivariate instrument standardization. _Anal. Chem._

67, 2379–2385 (1995). Article  CAS  Google Scholar  * Kennard, R. W. & Stone, L. A. Computer aided design of experiments. _Technometrics_ 11, 137–148 (1969). Article  Google Scholar  *

Palonpon, A. F. et al. Raman and SERS microscopy for molecular imaging of live cells. _Nat. Protoc._ 8, 677–692 (2013). Article  CAS  PubMed  Google Scholar  * Witze, E. S., Old, W. M.,

Resing, K. A. & Ahn, N. G. Mapping protein post-translational modifications with mass spectrometry. _Nat. Methods_ 4, 798–806 (2007). Article  CAS  PubMed  Google Scholar  * Aebersold,

R. & Mann, M. Mass spectrometry-based proteomics. _Nature_ 422, 198–207 (2003). Article  CAS  PubMed  Google Scholar  * Pence, I. & Mahadevan-Jansen, A. Clinical instrumentation and

applications of Raman spectroscopy. _Chem. Soc. Rev._ 45, 1958–1979 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Ibrahim, O. et al. Improved protocols for pre-processing

Raman spectra of formalin fixed paraffin preserved tissue sections. _Anal. Methods_ 9, 4709–4717 (2017). Article  CAS  Google Scholar  * Tfayli, A. et al. Digital dewaxing of Raman signals:

discrimination between nevi and melanoma spectra obtained from paraffin-embedded skin biopsies. _Appl. Spectrosc._ 63, 564–570 (2009). Article  CAS  PubMed  Google Scholar  * Byrne, H. J.,

Knief, P., Keating, M. E. & Bonnier, F. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. _Chem. Soc. Rev._ 45, 1865–1878 (2016).

Article  CAS  PubMed  Google Scholar  * Meade, A. D. et al. Studies of chemical fixation effects in human cell lines using Raman microspectroscopy. _Anal. Bioanal. Chem._ 396, 1781–1791

(2010). Article  CAS  PubMed  Google Scholar  * Baker, M. J. et al. Developing and understanding biofluid vibrational spectroscopy: a critical review. _Chem. Soc. Rev._ 45, 1803–1818 (2016).

Article  CAS  PubMed  Google Scholar  * Bonifacio, A., Cervo, S. & Sergo, V. Label-free surface-enhanced Raman spectroscopy of biofluids: fundamental aspects and diagnostic

applications. _Anal. Bioanal. Chem._ 407, 8265–8277 (2015). Article  CAS  PubMed  Google Scholar  * Mitchell, A. L., Gajjar, K. B., Theophilou, G., Martin, F. L. & Martin-Hirsch, P. L.

Vibrational spectroscopy of biofluids for disease screening or diagnosis: translation from the laboratory to a clinical setting. _J. Biophotonics_ 7, 153–165 (2014). Article  CAS  PubMed 

Google Scholar  * Lovergne, L. et al. Biofluid infrared spectro-diagnostics: pre-analytical considerations for clinical applications. _Faraday Discuss._ 187, 521–537 (2016). Article  CAS 

PubMed  Google Scholar  * Bonifacio, A. et al. Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic study. _Anal. Bioanal. Chem._ 406,

2355–2365 (2014). Article  CAS  PubMed  Google Scholar  * Paraskevaidi, M., Martin-Hirsch, P. L. & Martin, F. L. ATR-FTIR spectroscopy tools for medical diagnosis and disease

investigation. in _Nanotechnology Characterization Tools for Biosensing and Medical Diagnosis_ (ed. Kumar, C. S. S. R.) 163–211 (Springer, Berlin, 2017). * Mitchell, B. L., Yasui, Y., Li, C.

I., Fitzpatrick, A. L. & Lampe, P. D. Impact of freeze–thaw cycles and storage time on plasma samples used in mass spectrometry based biomarker discovery projects. _Cancer Inform._ 1,

98–104 (2005). Article  CAS  PubMed  Google Scholar  * Glassford, S. E., Byrne, B. & Kazarian, S. G. Recent applications of ATR FTIR spectroscopy and imaging to proteins. _Biochim.

Biophys. Acta_ 1834, 2849–2858 (2013). Article  CAS  PubMed  Google Scholar  * Kundu, J., Le, F., Nordlander, P. & Halas, N. J. Surface enhanced infrared absorption (SEIRA) spectroscopy

on nanoshell aggregate substrates. _Chem. Phys. Lett._ 452, 115–119 (2008). Article  CAS  Google Scholar  * Jones, S., Carley, S. & Harrison, M. An introduction to power and sample size

estimation. _Emerg. Med. J._ 20, 453–458 (2003). Article  CAS  PubMed  PubMed Central  Google Scholar  * Beebe, K. R., Pell, R. J. & Seasholtz, M. B. _Chemometrics: A Practical Guide_

Vol. 4 (Wiley, New York,1998). * Pavia, D. L., Lampman, G. M., Kriz, G. S. & Vyvyan, J. A. _Introduction to Spectroscopy_ (Cengage Learning, Belmont, CA, 2008). * Hastie, T., Tibshirani,

R. & Friedman, J. _The Elements of Statistical Learning: Data Mining, Inference, and Prediction_ 2nd edn (Springer, New York, 2009). * Bro, R. & Smilde, A. K. Principal component

analysis. _Anal. Methods_ 6, 2812–2831 (2014). Article  CAS  Google Scholar  * Martin, F. L. et al. Identifying variables responsible for clustering in discriminant analysis of data from

infrared microspectroscopy of a biological sample. _J. Comput. Biol._ 14, 1176–1184 (2007). Article  CAS  PubMed  Google Scholar  * Martens, H. & Martens, M. Modified Jack-knife

estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). _Food Qual. Prefer._ 11, 5–16 (2000). Article  Google Scholar  * Rousseeuw, P. J. &

Hubert, M. Robust statistics for outlier detection. _Wiley Interdiscip. Rev. Data Min. Knowl. Discov._ 1, 73–79 (2011). Article  Google Scholar  * Jiang, F., Liu, G., Du, J. & Sui, Y.

Initialization of K-modes clustering using outlier detection techniques. _Inf. Sci._ 332, 167–183 (2016). Article  Google Scholar  * Domingues, R., Filippone, M., Michiardi, P. &

Zouaoui, J. A comparative evaluation of outlier detection algorithms: experiments and analyses. _Pattern Recognit._ 74, 406–421 (2018). Article  Google Scholar  * Bakeev, K. A. _Process

Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries_ 2nd edn (John Wiley & Sons, Chichester, UK, 2010). * Kuligowski,

J., Quintás, G., Herwig, C. & Lendl, B. A rapid method for the differentiation of yeast cells grown under carbon and nitrogen-limited conditions by means of partial least squares

discriminant analysis employing infrared micro-spectroscopic data of entire yeast cells. _Talanta_ 99, 566–573 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Morais, C. L.

& Lima, K. M. Comparing unfolded and two-dimensional discriminant analysis and support vector machines for classification of EEM data. _Chemom. Intell. Lab. Syst._ 170, 1–2 (2017).

Article  CAS  Google Scholar  * Seasholtz, M. B. & Kowalski, B. The parsimony principle applied to multivariate calibration. _Anal. Chim. Acta_ 277, 165–177 (1993). Article  CAS  Google

Scholar  * Morais, C. L. & Lima, K. M. Principal component analysis with linear and quadratic discriminant analysis for identification of cancer samples based on mass spectrometry. _J.

Braz. Chem. Soc._ 29, 472–481 (2017). Google Scholar  * Brereton, R. G. & Lloyd, G. R. Partial least squares discriminant analysis: taking the magic away. _J. Chemom._ 28, 213–225

(2014). Article  CAS  Google Scholar  * Hibbert, D. B. Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016). _Pure Appl. Chem._ 88, 407–443 (2016). Article  CAS 

Google Scholar  * McCall, J. Genetic algorithms for modelling and optimisation. _J. Comput. Appl. Math._ 184, 205–222 (2005). Article  Google Scholar  * Soares, S. F. C., Gomes, A. A.,

Araujo, M. C. U., Galvão Filho, A. R. & Galvão, R. K. H. The successive projections algorithm. _Trends Anal. Chem._ 42, 84–98 (2013). Article  CAS  Google Scholar  * Kamandar, M. &

Ghassemian, H. Maximum relevance, minimum redundancy feature extraction for hyperspectral images. in _2010 18th Iranian Conference on Electrical Engineering: Proceedings_ 254–259 (IEEE,

Isfahan, Iran, 2010). * Sattlecker, M., Stone, N., Smith, J. & Bessant, C. Assessment of robustness and transferability of classification models built for cancer diagnostics using Raman

spectroscopy. _J. Raman Spectrosc._ 42, 897–903 (2011). Article  CAS  Google Scholar  * Guo, S. et al. Towards an improvement of model transferability for Raman spectroscopy in biological

applications. _Vib. Spectrosc._ 91, 111–118 (2017). Article  CAS  Google Scholar  * Luo, X. et al. Calibration transfer across near infrared spectrometers for measuring hematocrit in the

blood of grazing cattle. _J. Near Infrared Spectrosc._ 25, 15–25 (2017). Article  CAS  Google Scholar  * Vaughan, A. A. et al. Liquid chromatography–mass spectrometry calibration transfer

and metabolomics data fusion. _Anal. Chem._ 84, 9848–9857 (2012). Article  CAS  PubMed  Google Scholar  * Rodriguez, J. D., Westenberger, B. J., Buhse, L. F. & Kauffman, J. F.

Standardization of Raman spectra for transfer of spectral libraries across different instruments. _Analyst_ 136, 4232–4240 (2011). Article  CAS  PubMed  Google Scholar  * Yu, B., Ji, H.

& Kang, Y. Standardization of near infrared spectra based on multi-task learning. _Spectrosc. Lett._ 49, 23–29 (2016). Article  CAS  Google Scholar  * Ni, L., Han, M., Luan, S. &

Zhang, L. Screening wavelengths with consistent and stable signals to realize calibration model transfer of near infrared spectra. _Spectrochim. Acta A_ 206, 350–358 (2019). Article  CAS 

Google Scholar  * Hu, R. & Xia, J. Calibration transfer of near infrared spectroscopy based on DS algorithm. in _2011 International Conference on Electric Information and Control

Engineering_ (_ICEICE_) 3062–3065 (IEEE, Wuhan, China). * Forina, M. et al. Transfer of calibration function in near-infrared spectroscopy. _Chemom. Intellig. Lab. Syst._ 27, 189–203 (1995).

Article  CAS  Google Scholar  * Xiao, H. et al. Comparison of benchtop Fourier-transform (FT) and portable grating scanning spectrometers for determination of total soluble solid contents

in single grape berry (_Vitis vinifera_ L.) and calibration transfer. _Sensors_ 17, 2693 (2017). * Yahaya, O., MatJafri, M., Aziz, A. & Omar, A. Visible spectroscopy calibration transfer

model in determining pH of Sala mangoes. _J. Instrum._ 10, T05002 (2015). Article  CAS  Google Scholar  * Bin, J., Li, X., Fan, W., Zhou, J.-h & Wang, C.-w Calibration transfer of

near-infrared spectroscopy by canonical correlation analysis coupled with wavelet transform. _Analyst_ 142, 2229–2238 (2017). Article  CAS  PubMed  Google Scholar  * Monakhova, Y. B. &

Diehl, B. W. Transfer of multivariate regression models between high-resolution NMR instruments: application to authenticity control of sunflower lecithin. _Magn. Reson. Chem._ 54, 712–717

(2016). Article  CAS  PubMed  Google Scholar  * Zuo, Q., Xiong, S., Chen, Z.-P., Chen, Y. & Yu, R.-Q. A novel calibration strategy based on background correction for quantitative

circular dichroism spectroscopy. _Talanta_ 174, 320–324 (2017). Article  CAS  PubMed  Google Scholar  * Savitzky, A. & Golay, M. J. Smoothing and differentiation of data by simplified

least squares procedures. _Anal. Chem._ 36, 1627–1639 (1964). Article  CAS  Google Scholar  * Geladi, P., MacDougall, D. & Martens, H. Linearization and scatter-correction for

near-infrared reflectance spectra of meat. _Appl. Spectrosc._ 39, 491–500 (1985). Article  Google Scholar  * Barnes, R., Dhanoa, M. S. & Lister, S. J. Standard normal variate

transformation and de-trending of near-infrared diffuse reflectance spectra. _Appl. Spectrosc._ 43, 772–777 (1989). Article  CAS  Google Scholar  * Brereton, R. G. _Chemometrics: Data

Analysis for the Laboratory and Chemical Plant_ (John Wiley & Sons, Chichester, UK, 2003). * Dixon, S. J. & Brereton, R. G. Comparison of performance of five common classifiers

represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as

dependent on data structure. _Chemom. Intell. Lab. Syst._ 95, 1–17 (2009). Article  CAS  Google Scholar  * Cover, T. & Hart, P. Nearest neighbor pattern classification. _IEEE Trans. Inf.

Theory_ 13, 21–27 (1967). Article  Google Scholar  * Cortes, C. & Vapnik, V. Support-vector networks. _Mach. Learn._ 20, 273–297 (1995). Google Scholar  * Abraham, A. Artificial neural

networks. in _Handbook of Measuring System Design_ (eds Sydenham, P. H. & Thorn, R.) (John Wiley & Sons, Chichester, UK, 2005). * Fawagreh, K., Gaber, M. M. & Elyan, E. Random

forests: from early developments to recent advancements. _Syst. Sci. Control Eng._ 2, 602–609 (2014). Article  Google Scholar  * LeCun, Y., Bengio, Y. & Hinton, G. Deep learning.

_Nature_ 521, 436–444 (2015). Article  CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS C.L.M.M. thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) -

Brazil (grant 88881.128982/2016-01) for financial support. The work in the laboratory of F.L.M. was supported in part by The Engineering and Physical Sciences Research Council (EPSRC; grant

nos: EP/K023349/1 and EP/K023373/1). M.P. acknowledges the Rosemere Cancer Foundation for funding. AUTHOR INFORMATION Author notes * These authors contributed equally: Camilo L.M. Morais,

Maria Paraskevaidi. AUTHORS AND AFFILIATIONS * School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK Camilo L. M. Morais, Maria Paraskevaidi & Francis

L. Martin * Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China Li Cui & Yong-Guan Zhu * Division of Biomedical and Life

Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK Nigel J. Fullwood * Spectroscopy Products Division, Renishaw plc., New Mills, Wotton-under-Edge, UK Martin

Isabelle * Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil Kássio M. G. Lima * Department of Obstetrics and

Gynaecology, Lancashire Teaching Hospitals NHS Foundation, Preston, UK Pierre L. Martin-Hirsch * Department of Pathology, University of Illinois at Chicago, Chicago, IL, USA Hari Sreedhar 

& Michael J. Walsh * Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, São Paulo, Brazil Júlio Trevisan * School of Environment, Tsinghua University,

Beijing, China Dayi Zhang Authors * Camilo L. M. Morais View author publications You can also search for this author inPubMed Google Scholar * Maria Paraskevaidi View author publications You

can also search for this author inPubMed Google Scholar * Li Cui View author publications You can also search for this author inPubMed Google Scholar * Nigel J. Fullwood View author

publications You can also search for this author inPubMed Google Scholar * Martin Isabelle View author publications You can also search for this author inPubMed Google Scholar * Kássio M. G.

Lima View author publications You can also search for this author inPubMed Google Scholar * Pierre L. Martin-Hirsch View author publications You can also search for this author inPubMed 

Google Scholar * Hari Sreedhar View author publications You can also search for this author inPubMed Google Scholar * Júlio Trevisan View author publications You can also search for this

author inPubMed Google Scholar * Michael J. Walsh View author publications You can also search for this author inPubMed Google Scholar * Dayi Zhang View author publications You can also

search for this author inPubMed Google Scholar * Yong-Guan Zhu View author publications You can also search for this author inPubMed Google Scholar * Francis L. Martin View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS F.L.M. is the principal investigator who conceived and developed the idea for the article; C.L.M.M. and

M.P. wrote the manuscript. L.C., N.J.F., M.I., K.M.G.L., P.L.M.-H., H.S., J.T., M.J.W., D.Z. and Y.-G.Z. contributed recommendations and provided feedback and changes to the manuscript, and

C.L.M.M., M.P. and F.L.M. brought together the text and finalized the manuscript. CORRESPONDING AUTHORS Correspondence to Camilo L. M. Morais, Maria Paraskevaidi or Francis L. Martin.

ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION JOURNAL PEER REVIEW INFORMATION: _Nature Protocols_ thanks Åsmund Rinnan and other

anonymous reviewer(s) for their contribution to the peer review of this work. PUBLISHER’S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in published maps and

institutional affiliations. RELATED LINKS KEY REFERENCES USING THIS PROTOCOL Martin, F. L. et al. _Nat. Protoc_. 5, 1748–1760 (2010): https://doi.org/10.1038/nprot.2010.133 Baker, M. J. et

al. _Nat. Protoc_. 9, 1771–1791 (2014): https://doi.org/10.1038/nprot.2014.110 Medeiros de Morais, C. L. & de Lima, K. M. G. _Anal. Methods_ 7, 6904–6910 (2015):

https://doi.org/10.1039/C5AY01369K Vasconcelos de Andrade, E. W. et al. _Curr. Anal. Chem_. 14, 488–494 (2018): https://doi.org/10.2174/1573411014666171212141909 INTEGRATED SUPPLEMENTARY

INFORMATION SUPPLEMENTARY FIGURE 1 IR SPECTRA OF THE SAME TYPE OF SAMPLES MEASURED BY DIFFERENT ATR-FIR SPECTROMETERS AT THE SAME INSTITUTION. A–D, Average (A) raw and (B) preprocessed

spectra for healthy control samples, and average (C) raw and (D) preprocessed spectra for cancer samples across three different instruments (A, B and C). SUPPLEMENTARY FIGURE 2 PCA SCORES

FOR PREPROCESSED SPECTRA ACQUIRED BY DIFFERENT ATR-FIR SPECTROMETERS AT THE SAME INSTITUTION AND OUTLIER DETECTION TEST. A, PCA scores for healthy control samples according to the instrument

used for spectra acquisition (A, B and C). B, PCA scores for cancer samples according to the instrument used for spectra acquisition (A, B and C). C, Hotelling’s _T_2 versus _Q_ residuals

test for healthy control samples according to the instrument used for spectra acquisition (A, B and C) based on a PCA using 5 PCs (94.77% cumulative variance). D, Hotelling’s _T_2 versus _Q_

residuals test for cancer samples according to the instrument used for spectra acquisition (A, B and C) based on a PCA using 5 PCs (92.96% cumulative variance). Circled samples in C and D

indicate outliers removed. Confidence ellipse was 95%, depicted in blue in A and B. SUPPLEMENTARY FIGURE 3 PCA LOADINGS FOR PREPROCESSED SPECTRA ACQUIRED BY DIFFERENT ATR-FIR SPECTROMETERS

AT THE SAME INSTITUTION. A, PCA loadings for healthy control samples measured in different instruments (A, B and C). B, PCA loadings for cancer samples measured in different instruments (A,

B and C). SUPPLEMENTARY FIGURE 4 IR SPECTRA OF HEALTHY CONTROL SAMPLES MEASURED BY DIFFERENT OPERATORS AT THE SAME INSTITUTION. A,B, Average (A) raw and (B) pre-processed spectra for healthy

control samples acquired with instrument A depending on the operator. C,D, Average (C) raw and (D) preprocessed spectra for healthy control samples acquired with instrument B depending on

the operator. E,F, Average (E) raw and (F) preprocessed spectra for healthy control samples acquired with instrument C, varying the operator. SUPPLEMENTARY FIGURE 5 IR SPECTRA OF OVARIAN

CANCER SAMPLES MEASURED BY DIFFERENT OPERATORS AT THE SAME INSTITUTION. A,B, Average (A) raw and (B) preprocessed spectra for cancer samples acquired with instrument A depending on the

operator. C,D, Average (C) raw and (D) preprocessed spectra for cancer samples acquired with instrument B depending on the operator. E,F, Average (E) raw and (F) preprocessed spectra for

cancer samples acquired with instrument C depending on the operator. SUPPLEMENTARY FIGURE 6 PCA SCORES FOR PREPROCESSED SPECTRA ACQUIRED BY DIFFERENT OPERATORS AT THE SAME INSTITUTION. A,B,

PCA scores for (A) healthy control and (B) cancer samples acquired with instrument A depending on the operator. C,D, PCA scores for (C) healthy control and (D) cancer samples acquired with

instrument B depending on the operator. E,F, PCA scores for (E) healthy control and (F) cancer samples acquired with instrument C depending on the operator. Confidence ellipse was 95%,

depicted in blue. SUPPLEMENTARY FIGURE 7 OUTLIER DETECTION TEST FOR HEALTHY CONTROLS AND OVARIAN CANCER SAMPLES. A, Hotelling’s _T_2 versus _Q_ residuals test based on a PCA using 8 PCs

(99.07% cumulative variance) for healthy control samples depending on the instrument for spectra acquisition (A, B and C) used by operator 2. B, Hotelling’s _T_2 versus _Q_ residuals test

based on a PCA using 5 PCs (96.92% cumulative variance) for cancer samples depending on the instrument for spectra acquisition (A, B and C) used by operator 2. Circled sample in A indicates

an outlier removed. The Hotelling’s _T_2 versus _Q_ residuals test for operator 1 is depicted in Supplementary Fig. 2c,d. SUPPLEMENTARY FIGURE 8 PCA SCORES FOR HEALTHY CONTROLS (HC) AND

OVARIAN CANCER (OC) SAMPLES BASED ON THE SPECTRA ACQUIRED BY BOTH OPERATORS (1 AND 2) AND BY ALL INSTRUMENTS (A, B AND C). Confidence ellipse at a 95% confidence level is depicted in blue.

SUPPLEMENTARY INFORMATION SUPPLEMENTARY TEXT AND FIGURES Supplementary Figures 1–8 and Supplementary Methods REPORTING SUMMARY RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS

ARTICLE CITE THIS ARTICLE Morais, C.L.M., Paraskevaidi, M., Cui, L. _et al._ Standardization of complex biologically derived spectrochemical datasets. _Nat Protoc_ 14, 1546–1577 (2019).

https://doi.org/10.1038/s41596-019-0150-x Download citation * Received: 13 April 2018 * Accepted: 12 February 2019 * Published: 05 April 2019 * Issue Date: May 2019 * DOI:

https://doi.org/10.1038/s41596-019-0150-x SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not

currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative