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Yao W, Liu R, Zhang F, Li S, Huang X, Guo H, Peng M, Zhong G. Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy. Molecules 2022; 27:molecules27196294. [PMID: 36234831 PMCID: PMC9571819 DOI: 10.3390/molecules27196294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Aflatioxin B1 (AFB1) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB1-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB1 or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB1 in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (Rc2 = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB1 infection in the early stages.
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Affiliation(s)
- Wanqing Yao
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
| | - Ruanshan Liu
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Fengru Zhang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Shuang Li
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoxia Huang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Hongwei Guo
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Mengxia Peng
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Guohua Zhong
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
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Li G, Wang D, Wang K, Lin L. A two-dimensional sample screening method based on data quality and variable correlation. Anal Chim Acta 2022; 1203:339700. [DOI: 10.1016/j.aca.2022.339700] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/15/2022] [Accepted: 03/07/2022] [Indexed: 11/25/2022]
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Barra I, Kharbach M, Bousrabat M, Cherrah Y, Hanafi M, Qannari EM, Bouklouze A. Discrimination of diesel fuels marketed in Morocco using FTIR, GC-MS analysis and chemometrics methods. Talanta 2020; 209:120543. [DOI: 10.1016/j.talanta.2019.120543] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 11/04/2019] [Accepted: 11/05/2019] [Indexed: 10/25/2022]
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Lee LC, Liong CY, Jemain AA. Validity of the best practice in splitting data for hold-out validation strategy as performed on the ink strokes in the context of forensic science. Microchem J 2018. [DOI: 10.1016/j.microc.2018.02.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Shen F, Wu Q, Shao X, Zhang Q. Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques. Journal of Food Science and Technology 2018; 55:1175-1184. [PMID: 29487460 DOI: 10.1007/s13197-018-3033-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/02/2018] [Indexed: 11/28/2022]
Abstract
The applicability of near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was explored in this study to develop rapid, low-cost and non-destructive spectroscopic methods for classification and quantification of aflatoxins in brown rice. A total of 132 brown rice samples within the aflatoxin concentration range of 0-2435.8 μg/kg were prepared by artificially inoculated with A. flavus and A. parasiticus strains of fungus. For the classification of samples at varying levels of aflatoxin B1, the linear discriminant analysis model obtained correct classification rate of 96.9 and 90.6% for NIR and MIR spectroscopy, respectively. For the simultaneous determination of aflatoxins B1, B2, G1, G2 and the total aflatoxins, partial least squares regression also showed good predictive accuracy for both NIR (rv = 0.936-0.973, RPD = 2.5-4.0) and MIR spectroscopy (rv = 0.922-0.970, RPD = 2.5-4.0). The overall results indicated that the two spectroscopic techniques offered the feasibility to be used as alternative tools for rapid detection of various aflatoxin contaminations in grain.
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Affiliation(s)
- Fei Shen
- 1College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing, 210023 China
| | - Qifang Wu
- 1College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing, 210023 China
| | - Xiaolong Shao
- 1College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing, 210023 China
| | - Qiang Zhang
- 2Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6 Canada
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Vuiblet V, Fere M, Gobinet C, Birembaut P, Piot O, Rieu P. Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique. J Am Soc Nephrol 2015; 27:2382-91. [PMID: 26683669 DOI: 10.1681/asn.2015050601] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Accepted: 11/01/2015] [Indexed: 01/05/2023] Open
Abstract
Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.
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Affiliation(s)
- Vincent Vuiblet
- Matrice Extracellulaire et Dynamique Cellulaire Unit, Centre National pour la Recherche Scientifique, Unité Mixte de Recherche 7369, and Nephrology and Renal Transplantation Department and Biopathology Laboratory, Centre Hospitalier et Universitaire de Reims, Reims, France
| | - Michael Fere
- Matrice Extracellulaire et Dynamique Cellulaire Unit, Centre National pour la Recherche Scientifique, Unité Mixte de Recherche 7369, and
| | - Cyril Gobinet
- Matrice Extracellulaire et Dynamique Cellulaire Unit, Centre National pour la Recherche Scientifique, Unité Mixte de Recherche 7369, and
| | - Philippe Birembaut
- Biopathology Laboratory, Centre Hospitalier et Universitaire de Reims, Reims, France
| | - Olivier Piot
- Matrice Extracellulaire et Dynamique Cellulaire Unit, Centre National pour la Recherche Scientifique, Unité Mixte de Recherche 7369, and Cellular and Tissular Imaging Platform, Université de Reims Champagne-Ardenne, Reims, France; and
| | - Philippe Rieu
- Matrice Extracellulaire et Dynamique Cellulaire Unit, Centre National pour la Recherche Scientifique, Unité Mixte de Recherche 7369, and Nephrology and Renal Transplantation Department and
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Lima KMG, Gajjar KB, Martin-Hirsch PL, Martin FL. Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR-FTIR spectroscopy coupled with variable selection methods. Biotechnol Prog 2015; 31:832-9. [PMID: 25832726 DOI: 10.1002/btpr.2084] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 03/19/2015] [Indexed: 02/03/2023]
Abstract
Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II-IV; serous vs. non-serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real-world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II-IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA-LDA model with 33 wavenumbers. For serous vs. non-serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA-LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA-LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II-IV; up to 93.0% serous vs. non-serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population-based assessment of biomarkers signatures using ATR-FTIR spectroscopy as a screening tool for stage of ovarian cancer.
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Affiliation(s)
- Kássio M G Lima
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK.,Inst. of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, 59072-970, RN-Brazil
| | - Ketan B Gajjar
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK.,Dept. of Obstetrics and Gynaecology, Central Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | | | - Francis L Martin
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK
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Adutwum LA, Harynuk JJ. Unique Ion Filter: A Data Reduction Tool for GC/MS Data Preprocessing Prior to Chemometric Analysis. Anal Chem 2014; 86:7726-33. [DOI: 10.1021/ac501660a] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- L. A. Adutwum
- Department
of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2, Canada
| | - J. J. Harynuk
- Department
of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2, Canada
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