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The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications. [ABSTRACT FROM AUTHOR]

Determination of the optical properties of biological materials based on steady-state spatially resolved diffuse reflectance imaging is a complicated inverse problem-solving process. An effective inverse algorithm is first necessary and validated. This article proposed a Fourier series expansion (FSE) coupled with least squares support vector machine (LS-SVM) as an inverse algorithm for determining the absorption coefficient (μa) and the reduced scattering coefficient () of biological materials. A hyperspectral imaging system was used to acquire scattering images and steady-state spatially resolved diffuse reflectance profiles of liquid phantoms. Experiment results demonstrated that the hyperspectral imaging system coupled with this inverse algorithm effectively improved prediction accuracy of both μa and of liquid phantoms. Tests on liquid phantoms showed that the mean relative errors of this inverse algorithm to be 11.03% for the absorption coefficient and 7.16% for the reduced scattering coefficient when corresponding Fourier coefficients of liquid phantoms were used to develop the prediction model. To further study the method, the Fourier coefficients calculated from normalized Monte Carlo simulation data were used to develop the prediction model for determining the optical properties of 36 liquid phantoms. The prediction errors were 15.96 and 10.91% for μa and , respectively. For all liquid phantoms, it was found that the prediction values of both μa and were generally in good agreement with their actual values. Therefore, the FSE-LS-SVM method provides an effectively means for improving the prediction accuracy of optical properties of biological materials. [ABSTRACT FROM AUTHOR]

Abstract: Light interaction with turbid biological materials involves absorption and scattering. Quantitative understanding of light transport process and features in the fruit is critical to designing better optical systems for inspection of food quality. This paper reports on the quantification of light transport in the apple fruit in the visible and short-wavelength near-infrared region using Monte Carlo simulations. The absorption and reduced scattering coefficients (μ a and , respectively) of 600 ‘Golden Delicious’ apples were determined over the spectral range of 500–1000nm using a spatially resolved hyperspectral imaging method coupled with a diffusion theory model. The μ a and values were used in Monte Carlo (MC) models to simulate light transport in the fruit tissue. MC simulation models were validated against the diffusion theory model and experimental data. The patterns of diffuse reflectance, internal absorption, and light penetration depth were determined using typical values of μ a and for the apples. Simulation results showed that up to 96.4% of the photons were absorbed under the maximum absorption condition, while 75.9% photons exited as diffuse reflectance for the maximum scattering case. The optimum sensing range under our imaging system setup was found to be 1–11mm for ‘Golden Delicious’ apples. Fruit tissue with a larger μ a value absorbed light energy rapidly in short depth and radial distances, whereas light in the tissue with small values tended to propagate forward to the deeper area of the sample. Light penetration depths in ‘Golden Delicious’ apples, defined as the depths at which the incident light was reduced by 99%, were in the range of 0.43–8.67cm over the 500–1000nm spectral range, with a majority of the samples (approximately 68%) in the range of 0.81–4.48cm. Pigments and water in the fruit tissue greatly influenced light penetration depth. [Copyright &y& Elsevier]

Abstract: Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. This research was aimed to develop a hyperspectral imaging approach for detecting canker lesions on citrus fruit. A hyperspectral imaging system was developed for acquiring reflectance images from citrus samples in the spectral region from 450 to 930nm. Ruby Red grapefruits with cankerous, normal and other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. Spectral information divergence (SID) classification method, which was based on quantifying the spectral similarities by using a predetermined canker reference spectrum, was performed on the hyperspectral images of the grapefruits for differentiating canker from normal fruit peels and other citrus surface conditions. The overall classification accuracy was 96.2% using an optimized SID threshold value of 0.008, which was determined under the condition that the errors of false negative and false positive were weighted equally. Considering the high economic impact of missing a cankerous fruit, zero false negative error was achieved by using a threshold value of 0.009, under which the classification accuracy was 95.2%. This research demonstrated that hyperspectral imaging technique coupled with the SID based image classification method could be used for discriminating citrus canker from other confounding diseases. [Copyright &y& Elsevier]

This paper reports on the measurement of the optical properties of fresh fruits and vegetables over the visible and short-wave near-infrared region (500–1000nm) using a spatially resolved steady-state diffuse reflectance technique. A hyperspectral imaging system in line scan mode was used to acquire spatially resolved diffuse reflectance images from the samples of apple (three varieties), peach, pear, kiwifruit, plum, cucumber, zucchini squash, and tomato (at three ripeness stages) over the spectral range of 500–1000nm. The absorption and reduced scattering coefficients of the samples were determined from the spatially resolved scattering profiles using inverse algorithms for a diffusion theory model. Spectra of the absorption coefficient were featured by major pigments (chlorophyll, anthocyanin, and carotenoid) and water in the samples, whereas spectra of the reduced scattering coefficient generally decreased with the increase of wavelength. Values of the absorption and reduced scattering coefficients varied greatly among the test samples. Large differences in the absorption spectra were observed for the tomatoes of three ripeness stages (green, pink, and red), and their ripeness was correctly classified using the ratio of the absorption coefficient at 675nm (for chlorophyll) to that at 535nm (for anthocyanin). Values of the reduced scattering coefficient positively correlated with the firmness of tomatoes at individual wavelengths of 500–1000nm, with the maximum correlation of 0.66 being obtained at 790nm. Light penetration depths, defined as the depths at which the incident light was reduced by 99%, were estimated to be between 0.97 and 6.52cm for the fruit and vegetable samples over the wavelength range of 500–1000nm; they were influenced by major pigments in the plant tissue. The spatially resolved steady-state diffuse reflectance technique provides a convenient and efficient means for measuring the optical properties of turbid food and agricultural products. [Copyright &y& Elsevier]

Abstract\nHIGHLIGHTSFruit and vegetables (F&V) are vastly complicated products with highly diverse chemical and structural characteristics. Advanced imaging techniques either combine imaging with spectral information or can provide excellent tissue penetration, and enable the possibility to target, visualize and even qualify the chemical and physical (structural) heterogeneity within F&V. In this review, visible and/or near infrared hyperspectral imaging, Fourier transform infrared microspectroscopic imaging, Raman imaging, X-ray and magnetic resonance imaging to reveal chemical and structural information in a spatial context of F&V at the macro- (entire products), meso- (tissues), and micro- (individual cells) scales are comprehensively summarized. In addition, their basic concepts and operational procedures, particularly sample preparation and instrumental parameter adjustments, are addressed. Finally, future challenges and perspectives of these techniques are put forward. These imaging techniques are powerful tools to assess the biochemical and structural heterogeneity of F&V. Cost reduction, sensor fusion and data sharing platforms are future trends. More emphasis on aspects of knowledge and extension at the level of academia and research, especially on how to select techniques, choose operational parameters and prepare samples, are important to overcome barriers for the wider adoption of these techniques to improve the evaluation of F&V quality.Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention.Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention. [ABSTRACT FROM AUTHOR]

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling. • Multimode hyperspectral imaging techniques were used to authenticate fish fillets. • Different fish species and freshness showed differences in four types of spectra. • Fish species and freshness can be classified using spectral machine learning methods. • Visible-near-infrared reflectance can be used to develop a low-cost detection device. [ABSTRACT FROM AUTHOR]

Total volatile basic nitrogen (TVB-N) content is an important index used to evaluate the freshness of pork. In this paper, a strategy for measurement of TVB-N content in pork through hyperspectral imaging (HSI) (400–1000 nm) was developed. Firstly, image textural features based on Gabor filter and spectral features were obtained from the hyperspectral image after determining the region of interest. Then, nine feature wavelengths were selected using partial least-squares projection algorithm. And, major components were obtained from the 2D principal component analysis (2DPCA). Finally, a calibration model was established based on major components using least-squares support vector machine to predict TVB-N values. The results of two methods for data fusion, which are 2DPCA and principal component analysis (PCA), are compared. The correlation coefficients of prediction ( R P ) and root-mean-square errors of prediction (RMSEP) obtained through 2DPCA were 0.955 and 1.86 mg/100 g respectively, which was superior to the results based on PCA ( R P = 0.944, RMSEP = 2.07 mg/100 g). Compared to PCA, the residual prediction deviations (RPD) based on 2DPCA was raised from 3.01 to 3.35. Results demonstrated that the proposed model based on 2DPCA exhibited potential for nondestructive detection of TVB-N content in pork. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging technology combined with chemometrics is an effective nondestructive method for sorting seed varieties. However, the performance of the method is susceptible to the influence of time and depends on the training set used in the modeling process. The accuracy of classification models maybe deteriorate when they are used to differentiate the same variety of seeds harvested in different years, due to new variances in the test set are introduced by changes in the cultivation conditions, soil environmental conditions and climatic changes from one year to another. To maintain the accuracy and robustness of model, a model-updating algorithm for differentiating maize seed varieties from different years based on hyperspectral imaging coupled with a pre-labeling method was proposed in this work. The pre-label of each unlabeled sample was obtained using the original classification models developed by the least squares support vector machine classifier. The representative unlabeled samples, which had reliable pre-labels, were selected for updating classification models based on Pearson correlation coefficients. After model updating, the average classification accuracies were improved by 8.9%, 35.8% and 9.6%, compared with those of non-updated models for three test sets, respectively. This shows the effectiveness of the proposed method for classifying maize seeds of different years. [ABSTRACT FROM AUTHOR]

Since 2008, the detection of the adulterant melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02–1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938–1654 nm) of melamine powder, whole milk powder, nonfat milk powder, and mixtures of melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure melamine. Band ratio (B 1447/1466 ) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing melamine and melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of melamine in milk powders. [ABSTRACT FROM AUTHOR]

Illegal use of nitrogen-rich melamine (C 3 H 6 N 6 ) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography–mass spectrometry (GC–MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990–1700 nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. [ABSTRACT FROM AUTHOR]

Highlights: [•] Hyperspectral near-infrared imaging to detect low-concentration melamine in dry milk. [•] Visualization of melamine at 200ppm in milk powder without sample pretreatment. [•] Potential detection technique to screen food ingredients for multiple adulterants. [Copyright &y& Elsevier]

Aflatoxins, commonly found in corn and corn-derived products, can cause severe illness in animals and humans if consumed in significant amounts. Early detection is critical to preventing illness, but the most sensitive and effective of commonly used screening tools for aflatoxins are expensive and cumbersome methods based on chromatography or imunoassays that require technical expertise to perform. Multiple hyperspectral imaging techniques, including reflectance in the visible and near-infrared (VNIR) region and short-wave infrared (SWIR) region, fluorescence by 365 nm ultraviolet (UV) excitation, and Raman by 785 nm laser excitation, were used for detection of aflatoxin in ground maize. Four classification models based on linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines (QSVM) algorithms were developed for classification with each hyperspectral imaging mode. The multivariate classification models in combination with different preprocessing methods were applied for screening of maize samples naturally contaminated with aflatoxin. The classification accuracies for fluorescence with QSVM, VNIR with QSVM, SWIR with LSVM, and Raman with LSVM were 95.7%, 82.6%, 95.7%, and 87.0%, respectively, with no false-negative error at the cutoff of 10 μg/kg. The SWIR and fluorescence models showed slightly higher performance accuracies, suggesting that they may be more effective and efficient analytical tools for aflatoxin analysis in maize compared to conventional wet-chemical methods. These methods show promise as inexpensive, and easy-to-use screening tools for food safety, to rapidly detect aflatoxins in maize or other food ingredients intended for animal or human consumption. • Comparison of reflectance, fluorescence, and Raman imaging for aflatoxin in maize • Machine learning algorithms for analysis of hyperspectral images of ground maize • Higher classification accuracies from fluorescence and SWIR reflectance imaging • Easy-to-use screening tools to rapidly detect aflatoxin contamination for food safety [ABSTRACT FROM AUTHOR]