Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice

Sirisomboon, C Dachoupakan; Putthang, R; Sirisomboon, Panmanas

doi:10.1016/j.foodcont.2013.02.034

Cited by 70 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dachoupakan et al . proved the feasibility of determining the percentage of fungal infection in rice by NIR spectroscopy . Furthermore, Fantin et al .…”

Section: Introductionmentioning

confidence: 99%

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Wang

Pei

et al. 2020

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUND Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near‐infrared (NIR) spectroscopy, mid‐infrared (MIR) spectroscopy, and an electronic nose (E‐nose) were applied to detect and identify freeze‐dried Agaricus bisporus infected with the five fungi. RESULTS Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (Rc2) = 0.786, root mean‐square error of calibration (RMSEC) = 0.125 log CFU g−1. The A. flavus group had the highest prediction performance: coefficient of prediction (Rp2) = 0.821, root mean‐square error of prediction (RMSEP) = 0.083 log CFU g−1. The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E‐nose results for freeze‐dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA) using NIR, MIR, and E‐nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E‐nose methods. CONCLUSION Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E‐nose were proven to be effective in monitoring fungal contamination on freeze‐dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry

show abstract

“…Dachoupakan et al . proved the feasibility of determining the percentage of fungal infection in rice by NIR spectroscopy . Furthermore, Fantin et al .…”

Section: Introductionmentioning

confidence: 99%

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Wang

Pei

et al. 2020

J Sci Food Agric

View full text Add to dashboard Cite

show abstract

“…Near-infrared (NIR) or mid-infrared (MIR) spectroscopy techniques, both in combination or not with Fourier-transform (FT), are commonly used in a remarkably wide range of applications for the analysis of moisture, oil, fiber, starch, lipids, protein, yeast and bacteria in agricultural products [ 14 , 15 ]. In recent years, the potential of using IR spectroscopy for the detection of mycotoxins, including DON, ochratoxin A, fumonisins and aflatoxins, and mycotoxigenic fungal contamination in cereals and cereal products has been also demonstrated [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Among mycotoxins, the most investigated one was DON, mainly in Fusarium -damaged wheat kernels and ground wheat [ 16 , 18 , 19 , 20 , 22 , 23 , 24 , 34 ] and to a lesser extent in maize, barley and oat [ 17 , 21 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

Girolamo

Cervellieri

Visconti

et al. 2014

Toxins

View full text Add to dashboard Cite

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

show abstract

“…Generally, calibration models will perform better if the noise level can be minimized either through hardware improvements or data preprocessing [39]. Table 1 shows the discrimination performance between normal and black-heart samples after different spectral preprocessing methods.…”

Section: Spectra Preprocessing Methods Selectionmentioning

confidence: 99%

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Song

Sun

et al. 2016

Applied Sciences

View full text Add to dashboard Cite

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

show abstract

Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice

Cited by 70 publications

References 20 publications

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Contact Info

Product

Resources

About