Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method

Ong, Pauline; Tung, I. Chu; Chiu, Ching-Feng; Tsai, I‐Lin; Shih, Hsi-Chang; Chen, Suming; Chuang, Yung-Kun

doi:10.1016/j.foodcont.2022.108886

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…In order to better explain the potentialities of our method, we report some recent studies published in the last three years showing the great practical-applicative potentialities of the use of NIR in monitoring mycotoxin contamination in several matrices used in animal or human nutrition in Table 8 . In particular, the results obtained in our study were in line with the classification studies reported below [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. The study reported here gave a practical application of NIR calibration in order to underline the importance of this type of approach as a practical tool to assess the safety of silage.…”

Section: Discussionsupporting

confidence: 92%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

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Section: Discussionsupporting

confidence: 92%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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“…To ensure the desired content of aflatoxin was spiked onto the peanuts, 16 peanuts spiked with 50, 100, 200, and 400 ppb were randomly selected after spectral acquisition to verify the aflatoxin content. An uHPLC-MS/MS method with Quick Easy Cheap Effective Rugged and Safe (QuEChERS) pretreatment was used with some modifications [ 15 , 28 , 29 , 30 ]. A single peanut kernel was placed into a 50 mL centrifuge tube; 10 μL of aflatoxin M 1 (10 μg/mL, Sigma-Aldrich, St. Louis, MO, USA) was added as internal standard (IS); and 20 mL of hexane was added and blended using a hand homogenizer (D100, Benchmark Scientific Inc, Sayreville, NJ, USA) in the tube.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous methods for assessing fungal-contaminated crops have been developed to confirm that aflatoxin levels remain below regulatory limits, such as thin-layer chromatography [ 11 ], high-pressure liquid chromatography [ 12 ], immunoaffinity columns [ 13 ], and enzyme-linked immunosorbent assays [ 14 , 15 ]. However, the laborious, slow, complex, and expensive sample preparation required for these analytical methods limits the nondestructive and real-time assessments of the large number of peanuts consumed every day [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman)

Yao,

Miyagusuku-Cruzado,

West

et al. 2024

Foods

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A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb–400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb–400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

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“…The simulated annealing algorithm (SA) is widely known as a meta-heuristic algorithm. It is generated from the physical annealing process of solids in metallurgy [27]. SA starts with a higher initial temperature.…”

Section: Feature Bands Selectionmentioning

confidence: 99%

Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China

Wang

Xie

et al. 2022

Remote Sensing

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Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible.

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Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method

Cited by 16 publications

References 39 publications

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman)

Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China

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