Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

2019 ◽  
Vol 82 (5) ◽  
pp. 796-803
Author(s):  
R. PUTTHANG ◽  
P. SIRISOMBOON ◽  
C. DACHOUPAKAN SIRISOMBOON

ABSTRACT The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B1 (AFB1) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB1 and 15 samples artificially contaminated with AFB1. Quantitative calibration models to detect AFB1 were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of −0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB1 contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB1 contamination.

2020 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05–0.07, 0.04–0.06, 0.82–0.92, and 2.3–3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.


2019 ◽  
Vol 28 (2) ◽  
pp. 59-69
Author(s):  
Feifei Tao ◽  
Haibo Yao ◽  
Zuzana Hruska ◽  
Yongliang Liu ◽  
Kanniah Rajasekaran ◽  
...  

In this study, visible-near infrared spectroscopy over the spectral range of 400–2500 nm was utilized to detect surface contamination of corn kernels with aflatoxin B1. The artificially contaminated samples were prepared by dropping known amounts of aflatoxin B1 standard dissolved in 50:50 ( v/ v) methanol/water solution, onto corn kernel surface to achieve different contamination levels of 10, 20, 50, 100, 500, and 1000 ppb. Both endosperm and germ sides of corn kernels were used for artificial contamination, and a total of 210 contaminated and control kernels were scanned with the visible-near infrared spectroscopy in reflectance mode. Spectral preprocessing methods including standard normal variate, first derivative, second derivative, first derivative + standard normal variate, and second derivative + standard normal variate were applied on the original absorbance spectra. Using the original and preprocessed spectra, the 3-class and 7-class discriminant models were established by the chemometric methods of principal component analysis-linear discriminant analysis and partial least squares discriminant analysis separately. The results showed that in discriminating the aflatoxin B1 contamination levels, the spectral range II (1120–2470 nm) generally performed better than using the corresponding spectra type over range I (410–1070 nm). Compared to using the original spectra, the first derivative and second derivative spectra generally improved the performance of the classification models. For classification thresholds of 20 and 100 ppb in aflatoxin B1, the best 3-class models achieved the same overall accuracy of 98.6% for prediction over both ranges I and II. For the 7-class discriminant models, the best overall accuracies obtained over ranges I and II are 91.4 and 97.1% for prediction.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Cheng LI ◽  
Bangsong SU ◽  
Tianlun ZHAO ◽  
Cong LI ◽  
Jinhong CHEN ◽  
...  

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and high-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05∼0.07, 0.04∼0.06, 0.82∼0.92, and 2.3∼3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in a good determination of gossypol content in intact cottonseeds. Conclusions Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares (PLS) regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and nondestructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.


2014 ◽  
Vol 07 (04) ◽  
pp. 1450002 ◽  
Author(s):  
Kannapot Kaewsorn ◽  
Panmanas Sirisomboon

Germinated brown rice (GBR) is rich in gamma oryzanol which increase its consumption popularity, particularly in the health food market. The objective of this research was to apply the near infrared spectroscopy (NIRS) for evaluation of gamma oryzanol of the germinated brown rice. The germinated brown rice samples were prepared from germinated rough rice (soaked for 24 and 48 h, incubated for 0, 6, 12, 18, 24, 30 and 36 h) and purchased from local supermarkets. The germinated brown rice samples were subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extraction methodology. The prediction model was established by partial least square regression (PLSR) and validated by full cross validation method. The NIRS model established from various varieties of germinated brown rice bought from different markets by first derivatives+vector normalization pretreated spectra showed the optimal prediction with the correlation of determination (R2), root mean squared error of cross validation (RMSECV) and bias of 0.934, 8.84 × 10-5 mg/100 g dry matter and 1.06 × 10-5 mg/100 g dry matter, respectively. This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice. This information is very useful to the germinated brown rice production factory and consumers.


2021 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  

Abstract Background: Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method.Results: Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05-0.07, 0.04-0.06, 0.82-0.92, and 2.3-3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions: Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.


2019 ◽  
Vol 4 (2) ◽  
pp. 397-406
Author(s):  
Puji Meihani ◽  
Agus Arip Munawar ◽  
Devianti Devianti

Abstrak. Penelitian ini bertujuan untuk mendeteksi pencemaran tanah (zat Pb, Zn dan Cu) dengan menggunakan NIRS. Metode yang dilakukan ialah skala laboratorium dan hasil uji menggunakan NIRS. Pada pengujian menggunakan NIRS, metode koreksi spektrum yang digunakan ialah Standard Normal Variate (SNV) dan De-Trending (DT) sedangkan dalam membangun model prediksi, metode regresi yang digunakan yakni Partial Least Square (PLS). Keakuratan model prediksi dilihat berdasarkan parameter statistika seperti r, R2, RMSEC dan RPD. Hasil yang didapatkan pada pengujian menggunakan NIRS pada prediksi data mentah untuk ketiga parameter (Pb, Zn dan Cu) didapatkan nilai RPD masing-masing 2.69, 2.69, dan 2.68. Nilai tersebut termasuk ke dalam kategori good model performance. Untuk meningkatkan nilai RPD, dilakukan prediksi setelah dikoreksi menggunakan SNV. Nilai RPD yang didapatkan pada masing-masing parameter (Pb, Zn dan Cu) adalah 5.21, 4.56, dan 4.78. Nilai-nilai prediksi tersebut masuk ke dalam kategori very good performance. Sedangkan nilai RPD untuk prediksi menggunakan SNV untuk ketiga parameter (Pb, Zn dan Cu) masing-masing 4.31, 4.39 dan 4.08 yang dikategorikan sebagai very good performance. Berdasarkan nilai RPD yang didapatkan dari ketiga prediksi, prediksi dengan menggunakan SNV yang paling baik karena memiliki nilai RPD yang paling tinggi.The Application of Near Infrared Spectroscopy (NIRS) to Soil Contamination DetectionAbstract. This study aims to soil pollution detection (Pb, Zn and Cu substances) by using NIRS. The method used are the laboratory scale and using NIRS. In using NIRS method, the spectrum correction method used is Standard Normal Variate (SNV) and De-Trending (DT). Prediction model using Partial Least Square (PLS). The accuracy of the prediction model is based on the statistical parameters such as r, R2, RMSEC and RPD. The results based on the NIRS method obtained the values of RPD are 2.69, 2.69, and 2.68 in prediction of raw data for parameters (Pb, Zn and Cu). These values belong to good model performance category. To increase the RPD score, prediction were made by using SNV spectrum correction method. RPD values obtained in each parameter (Pb, Zn and Cu) were 5.21, 4.56, and 4.78. These predictive values can be categorized as very good performance. The values of RPD for prediction used DT for the three parameters (Pb, Zn and Cu) 4.31, 4.39 and 4.08 which are categorized as very good performance. Based on RPD values obtained from the three predictions, predictions using SNV are the best because it has the highest RPD value.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.


Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.


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