Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

Wongthip

Journal of Near Infrared Spectroscopy

2019

Brown rice is a main popular health food with high nutritional value and health benefits. As a result of poor post-harvest drying and inappropriate storage conditions, rice grains are often damaged through fungal spoilage as well as mycotoxin production. The objective of this research was to evaluate the possibility of using the near infrared spectroscopy, with a wavenumber range between 12500 and 4000 cm À1 (800-2500 nm), as a rapid method for detection of aflatoxins in brown rice. Firstly, storage trials were carried out to generate representative of samples contaminated and non-contaminated with aflatoxins. These data were used to create a partial least squares regression model using 120 brown rice samples with the required near infrared spectral data and aflatoxin concentration levels that were determined using a standard enzymelinked immunosorbent assays method. The accuracy of developed models was externally validated using the test set. The statistical model developed from the treated spectra (vector normalization; SNV) provided the best accuracy in prediction with a coefficient of determination of prediction (r 2) of 0.95, a root mean square error of prediction of 415.00 mg kg À1 and a bias À54.00 mg kg À1. The model developed showed good predictive performance which suggests that it could have practical applications as a rapid method to detect aflatoxins in brown rice.

Section: Resultssupporting

confidence: 88%

Potential of near infrared spectroscopy as a rapid method to detect aflatoxins in brown rice

Wongthip

Journal of Near Infrared Spectroscopy

2019

Journal of Near Infrared Spectroscopy

“…The absorbance spectral pattern was observed to be in accordance with those reported in other studies on corn kernels. 3,7,8 Overall, the mean absorbance spectra of corn kernels from different groups showed analogous spectral features. Over the spectral range I, small absorbance peaks appeared in the visible spectral range, around 416, 452, and 492 nm, which are attributed to the absorbance of pigments contained in corn kernels.…”

Section: Resultsmentioning

confidence: 82%

“…2 Aflatoxin management strategies include biological control, predictive modeling, innovative storage techniques, breeding efforts, aflatoxin reduction strategies, and detection. 3 Effective detection plays a key role in reducing the risk of aflatoxins entering the food and feed chains. Rapid detection of aflatoxins is important for prompt intervention.…”

Section: Introductionmentioning

confidence: 99%

Detection of aflatoxin B₁ on corn kernel surfaces using visible-near infrared spectroscopy

Tao

Yao

Hruska

et al. 2019

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.

“…In recent studies, through the use of near-infrared spectroscopy technology, the vitality [5], internal essential constituents such as lipids [6], starches [7,8] and the toxin-infected pests [9,10,11] to corn seed batches have been studied, all of which have a rapid non-destructive advantage, but this technology only processes the corn seed in batches, and it is hard to determine the characteristics of individual corn seeds.…”

Section: Introductionmentioning

confidence: 99%

Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging

2019

A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.