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5. Detection of aflatoxin B1 in chili powder using attenuated total reflectance–Fourier transform infrared spectroscopy
| 發布日期:2024-06-12 | 更新日期:2024-06-12 發布單位:

Detection of aflatoxin B1 in chili powder using attenuated total reflectance–Fourier transform infrared spectroscopy

Thin Thin Seina,b,†, Urairat Mongmonsinb,†, Patutong Chatchawalc, Molin Wongwattanakulc,e, Oranee Srichaiyapolc, Rungrueang Pattanakuld, Patcharaporn Tippayawatb,e, *

aMedical Technology Program, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand, 40002.

bCenter for Research and Development of Medical Diagnostic Laboratory (CMDL), Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand, 40002.

cCenter for Innovation and Standard for Medical Technology and Physical Therapy (CISMaP), Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand, 40002.

dSynchrotron Light Research Institute (SLRI), 111 University Avenue, Muang District, Nakhon Ratchasima, Thailand, 30000.

eDepartment of Medical Technology, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand, 40002.

Aflatoxin B1, a major global food safety concern, is produced by toxigenic fungi during crop growing, drying, and storage, and shows increasing annual prevalence. This study aimed to detect aflatoxin B1 in chili samples using ATR–FTIR coupled with machine learning algorithms. We found that 83.6% of the chili powder samples were contaminated with Aspergillus and Penicillium species, with aflatoxin B1 levels ranging from 7.63 to 44.32 µg/kg. ATR–FTIR spectroscopy in the fingerprint region (1800−400 cm-1) showed peak intensity variation in the bands at 1587, 1393, and 1038 cm-1, which are mostly related to aflatoxin B1 structure. The PCA plots from samples with different trace amounts of aflatoxin B1 could not be separated. Vibrational spectroscopy combined with machine learning was applied to address this issue. The logistic regression model had the best F1 score with the highest %accuracy (73%), %sensitivity (73%), and %specificity (71%), followed by random forest and support vector machine models. Although the logistic regression model contributed significant findings, this study represents a laboratory research project. Because of the peculiarities of the ATR–FTIR spectral measurements, the spectra measured for several batches may differ, necessitating running the model on multiple spectral ranges and using increased sample sizes in subsequent applications. This proposed method has the potential to provide rapid and accurate results and may be valuable in future applications regarding toxin detection in foods when simple onsite testing is required.

Keywords: Aflatoxin B1; Attenuated total reflection−Fourier transform infrared spectroscopy (ATR–FTIR); Chili powder; Machine learning; Principal component analysis
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