Shortwave Infrared Hyperspectral Imaging Research Articles

Prediction of pelargonidin-3-glucoside in strawberries according to the postharvest distribution period of two ripening stages using VIS-NIR and SWIR hyperspectral imaging technology

The quality characteristics and consumer acceptability of strawberries depend on their ripening stages. Pelargodinin-3-glucoside (P3G) is the main anthocyanin in strawberries and changes during postharvest distribution. Hyperspectral imaging technology (HSI), which covers the ranges of visible-near infrared (VIS-NIR) and shortwave infrared (SWIR) light, was used to predict the P3G of strawberries for two types of harvest maturity, half maturity (HM) and full maturity (FM) during distribution periods. Spectral data was extracted from hyperspectral images of strawberries at the two stages of maturity and partial least square (PLS) regression was used to develop the prediction model of P3G. The highest prediction accuracy was 86% in HM using the SWIR region. Meaningful wavelengths to develop the PLS model were approximately 970, 1,153, 1,303, and 1,445 nm for moisture, carbohydrates, and anthocyanin. Moreover, the spatial information of P3G accumulation can be also analyzed using HSI. The overall results indicated that HSI could be used for the prediction of P3G with different harvest maturity of strawberry. Additionally, this technology could provide the information of distribution periods of strawberry by monitoring changes of pigment of strawberries.

Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork

Monitoring and maintaining the freshness of meat is important to ensuring a supply of meat that is safe for consumption. The objective of this study is to present a shortwave infrared (SWIR) hyperspectral imaging system in combination with partial least-squares regression (PLSR) model and feature selection methods that can be used for the prediction of the total volatile basic nitrogen (TVB-N) content in fresh pork. The SWIR hyperspectral reflectance images were acquired for pork samples removed from refrigerated storage after 1, 4, 8, 11, 15, and 21 days. The hyperspectral SWIR images and actual TVB-N contents were used for constructing the PLSR model. PLSR models were optimized by using feature selection strategies such as random frog (RF) and variable importance in projection (VIP) score. The predictions from the optimal RF-PLSR model value with maximum normalization preprocessing exhibited correlation coefficient values for Rc2 and Rp2 of 0.94 and 0.90, respectively. Moreover, this research showed that visualization of TVB-N levels applied to the optimal model based on selected wavebands provide an intuitive way to interpret the spatial information of the sample. This study revealed that the multivariate models developed here for rapid and nondestructive evaluation of pork freshness can be feasible for use in online inspection systems as an effective substitute for traditional methods to evaluate pork freshness.

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method.

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

Comparison of Vis-NIR and SWIR hyperspectral imaging for the non-destructive detection of DON levels in Fusarium head blight wheat kernels and wheat flour

Fusarium head blight is a severe wheat disease that affects yield and food safety. Chemical and biological methods are the main techniques for detection of the deoxynivalenol (DON) content, but despite being highly accurate, these techniques are destructive and time-consuming. A nondestructive, efficient and accurate method for estimating the DON content levels of scabs is required in post-harvest wheat processing. Hyperspectral imaging (HSI) systems have the capability of detecting subsurface defects and the contents or properties of organic substances. Because this method is nondestructive and fast and can be fully automated, the HSI technique was used in this study for detection of the wheat grain quality. Mycotoxins can destroy tissues, cells, and even molecules in cereals. Furthermore, the contents or properties of organic substances such as proteins, carbohydrates, and lipids in wheat infected by DON toxin undergo changes that can be reflected in their spectral characteristics. In this study, data for both wheat kernels and wheat flour obtained using two types of HSI systems, i.e., visible near-infrared (Vis-NIR) (ranging from 400 to 1000 nm) and short-wave infrared (SWIR) (ranging from 1000 to 2500 nm), were investigated. Ninety-six samples of wheat kernels and wheat flour samples were divided into two groups, and the multiplicative scatter correction (MSC) and standard normal variate (SNV) were used for spectral data pre-processing. A support vector machine (SVM) and a sparse autoencoder (SAE) network were used to the build classifiers with the extracted important wavelengths by genetic algorithm (GA). The results suggest that it is possible to detect wheat kernel samples in the Vis-NIR bands and to detect wheat flour samples from SWIR data with high accuracy. Moreover, for wheat kernel samples, the results of the SAE and SVM models were roughly the same. For all types of classification models for wheat kernel samples using the Vis-NIR, the MSC-GA-SAE model achieved the highest prediction accuracy (100% for both the training and test sets). In contrast to the wheat kernels samples, for the wheat flour samples, the SNV-GA-SAE models achieved better results with the SWIR data in terms of correctly classifying the samples (100% for the training set and 96% for the test set), which suggested that it is possible to detect wheat flour samples from SWIR data with high accuracy.

Ancient Greek text concealed on the back of unrolled papyrus revealed through shortwave-infrared hyperspectral imaging.

Only a few Herculaneum rolls exhibit writing on their reverse side. Since unrolled papyri are permanently glued to paperboard, so far, this fact was known to us only from 18th-century drawings. The application of shortwave-infrared (SWIR; 1000-2500 nm) hyperspectral imaging (HSI) to one of them (PHerc. 1691/1021) has revealed portions of Greek text hidden on the back more than 220 years after their first discovery, making it possible to recover this primary source for the ongoing new edition of this precious book. SWIR HSI has produced better contrast and legibility even on the extensive text preserved on the front compared to former imaging of Herculaneum papyri at 950 nm (improperly called multispectral imaging), with a substantial impact on the text reconstruction. These promising results confirm the importance of advanced techniques applied to ancient carbonized papyri and open the way to a better investigation of hundreds of other such papyri.

Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics.

Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the calibration accuracy of an instrument’s spectral response functions (SRF). The higher the calibration uncertainty of SRF, the worse the retrieval accuracy of the spectral characteristics of the targets. In this paper, an improved spectral calibration method based on a monochromator and the spectral absorptive characteristics of water vapor in the laboratory is presented. The water vapor spectral calibration method (WVSCM) uses the difference function to calculate the intrinsic DN response functions of the spectral channels located in the absorptive wavelength range of water vapor and corrects the wavelength offset of the monochromator via the least-square procedure to achieve spectral calibration throughout the full spectral responsive range of the hyper-spectrometer. The absolute spectral calibration uncertainty is ±0.125 nm. We validated the effectiveness of the WVSCM with two tunable semiconductor lasers, and the spectral wavelength positions calibrated by lasers and the WVSCM showed a good degree of consistency.

Non-contact and non-destructive detection and identification of Bacillus anthracis inside paper envelopes

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Shortwave Infrared Hyperspectral Imaging Can Predict Moisture Content in Grafted Cucumber Seedlings

This study aimed to develop statistical model for predicting the moisture content in grafted cucumber (Cucumis sativus L.) by analyzing shortwave infrared hyperspectral images of scions of cucumber seedlings in the range of 1,000-2,400 nm. These images were processed using brightness correction and two sets of data were created by normalizing the spectral reflectance and conducting the first derivative. Statistical analysis of the spectral reflectance and moisture content of the grafted cucumber scions was used to interpret the correlation coefficient spectrum, multiple linear regression, and partial least square (PLS) regression to select the significant wavelengths and develop a model that could predict moisture content. The normalized PLS regression model showed better prediction performance than the other analysis methods tested. The determination coefficient and the root mean square difference for the validation dataset were 0.66 and 1.25%, respectively. These results suggest that the moisture content of grafted cucumber seedlings can be predicted in a nondestructive manner using hyperspectral images with a proper model.

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

In this study, ultra-violet (UV) and short-wave infra-red (SWIR) hyperspectral imaging (HSI) was used to measure the concentration of phenolic flavour compounds on malted barley that are responsible for smoky aroma of Scotch whisky. UV-HSI is a relatively unexplored technique that has the potential to detect specific absorptions of phenols. SWIR-HSI has proven to detect phenols in previous applications. Support Vector Machine Classification and Regression was applied to classify malts with ten different concentration levels of the compounds of interest, and to estimate the concentration respectively. Results reveal that UV-HSI is at its current development stage unsuitable for this task whereas SWIR-HSI is able to produce robust results with a classification accuracy of 99.8% and a squared correlation coefficient of 0.98 with a Root Mean Squared Error of 0.32 ppm for regression. The results indicate that with further testing and development, HSI may potentially be exploited in an industrial production environment.

Detection of melamine in milk powder using MCT-based short-wave infrared hyperspectral imaging system

ABSTRACTExtensive research has been conducted on non-destructive and rapid detection of melamine in powdered foods in the last decade. While Raman and near-infrared hyperspectral imaging techniques have been successful in terms of non-destructive and rapid measurement, they have limitations with respect to measurement time and detection capability, respectively. Therefore, the objective of this study was to develop a mercury cadmium telluride (MCT)-based short-wave infrared (SWIR) hyperspectral imaging system and algorithm to detect melamine quantitatively in milk powder. The SWIR hyperspectral imaging system consisted of a custom-designed illumination system, a SWIR hyperspectral camera, a data acquisition module and a sample transfer table. SWIR hyperspectral images were obtained for melamine-milk samples with different melamine concentrations, pure melamine and pure milk powder. Analysis of variance and the partial least squares regression method over the 1000–2500 nm wavelength region were used to develop an optimal model for detection. The results showed that a melamine concentration as low as 50 ppm in melamine-milk powder samples could be detected. Thus, the MCT-based SWIR hyperspectral imaging system has the potential for quantitative and qualitative detection of adulterants in powder samples.

Non-destructive quality assessment of herbal tea blends using hyperspectral imaging

The consumption of herbal teas is increasing as consumers become more appreciative of the health benefits. Herbal tea blends comprise of two or more plant species blended to improve taste and multiply the health benefits. Quality control (QC) of herbal teas like other nutraceuticals, is important to ensure safety and efficacy. Current QC methods are chromatography-based and require destructive sample preparation using solvents. In this study, hyperspectral imaging is applied as a fast and non-destructive method for the quality control of herbal tea blends. The technique combines conventional spectroscopy and digital imaging to gather chemical information and visualise spatial distribution of chemical constituents within a matrix. Certified raw materials (Sceletium tortuosum and Cyclopia genistoides) and herbal tea blends were acquired from Parceval Pty (Ltd). Hyperspectral images of the raw material and tea blends were captured on a SisuChema® SWIR (short wave infrared) hyperspectral pushbroom imaging system using ChemaDAQ® software. The images were analysed using Evince® multivariate analysis software 2.4.0. Principal component analysis (PCA) revealed 54.2% chemical variation between S. tortuosum and C. genistoides raw materials. A partial least squares-discriminant analysis (PLS-DA) model with predictive ability of 95.8% was developed. Based on pixel classification, it was possible to visualise the tea blend constituents as S. tortuosum and C. genistoides and quantitatively predict C. genistoides as the major constituent (>97%) while S. tortuosum was present in lower amounts (<3%). The predictions confirm that HSI is a potentially favourable visual tool for the quality assessment of herbal tea blends. However, due to low instrument sensitivity quantitative determinations showed some deviation from the company formulation.

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

A short-wave infrared (SWIR) hyperspectral imaging system (1000–2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

Hyperspectral Imaging and Support Vector Machine: A Powerful Combination to Differentiate Black Cohosh (Actaea racemosa) from Other Cohosh Species.

Actaea racemosa (black cohosh) has a history of traditional use in the treatment of general gynecological problems. However, the plant is known to be vulnerable to adulteration with other cohosh species. This study evaluated the use of shortwave infrared hyperspectral imaging (SWIR-HSI) in tandem with chemometric data analysis as a fast alternative method for the discrimination of four cohosh species (Actaea racemosa, Actaea podocarpa, Actaea pachypoda, Actaea cimicifuga) and 36commercial products labelled as black cohosh. The raw material and commercial products were analyzed using SWIR-HSI and ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) followed by chemometric modeling. From SWIR-HSI data (920 - 2514 nm), the range containing the discriminating information of the four species was identified as 1204 - 1480 nm using Matlab software. After reduction of the data set range, partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models with coefficients of determination (R2 ) of ≥ 0.8 were created. The novel SVM-DA model showed better predictions and was used to predict the commercial product content. Seven out of 36 commercial products were recognized by the SVM-DA model as being true black cohosh while 29 products indicated adulteration. Analysis of the UHPLC-MS data demonstrated that six commercial products could be authentic black cohosh. This was confirmed using the fragmentation patterns of three black cohosh markers (cimiracemoside C; 12-β,21-dihydroxycimigenol-3-O-L-arabinoside; and 24-O-acetylhydroshengmanol-3-O-β-D-xylopyranoside). SWIR-HSI in conjunction with chemometric tools (SVM-DA) could identify 80% adulteration of commercial products labelled as black cohosh.

Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging

Bruises in apples is one of the most important quality factors during postharvest, which needs to be detected early and efficiently during sorting processes. In this study, a step-wise pixel based apple bruise detection system based on line scan hyperspectral imaging (HSI) in the shortwave infrared (SWIR) is demonstrated for three apple cultivars: ‘Jonagold’, ‘Kanzi’ and ‘Joly Red’. The SWIR HSI system performance was tested on apples from the different cultivars bruised at five different impact levels, and monitored from 1 to 36h after bruising. While glare regions are commonly considered as anomalies and discarded from further analysis, their spectral signatures enabled in this work to distinguish between cultivars with a prediction accuracy up to 96%. Different partial least squares-discriminant analysis (PLS-DA) models were trained to discriminate cultivars and then to discriminate between sound, bruised, glossy and stem regions. Spectral area normalization pre-processing was found to be the most effective for pixel based bruise prediction, resulting in a prediction accuracy up to 90.1%. Post-processing of the binary images by exploiting spatial information further improved the bruise detection accuracy to 94.4%.

Augmented design and analysis of computer experiments: a novel tolerance embedded global optimization approach applied to SWIR hyperspectral illumination design.

A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.

Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts.

It's well-known fungi-contaminated peanuts contain potent carcinogen. Efficiently identifying and separating the contaminated can help prevent aflatoxin entering in food chain. In this study, shortwave infrared (SWIR) hyperspectral images for identifying the prepared contaminated kernels. Feature selection method of analysis of variance (ANOVA) and feature extraction method of nonparametric weighted feature extraction (NWFE) were used to concentrate spectral information into a subspace where contaminated and healthy peanuts can have favorable separability. Then, peanut pixels were classified using SVM. Moreover, image segmentation method of region growing was applied to segment the image as kernel-scale patches and meanwhile to number the kernels. The result shows that pixel-wise classification accuracies are 99.13% for breed A, 96.72% for B and 99.73% for C in learning images, and are 96.32%, 94.2% and 97.51% in validation images. Contaminated peanuts were correctly marked as aberrant kernels in both learning images and validation images.

Shortwave Infrared Hyperspectral Imaging: A Novel Method For Enhancing the Visibility of Sedimentary And Biogenic Features In Oil-Saturated Core

A common challenge of logging core from heavy-oil reservoirs is that sedimentary and biogenic features are difficult to see in fine-grained, well-sorted, and oil-saturated strata. In this study, hyperspectral imaging is shown to be an effective method for enhancing the visibility of sedimentary fabric and trace fossils in oil-saturated core. Shortwave infrared (SWIR) hyperspectral imagery of Lower Cretaceous McMurray Formation oil-sands core from northeastern Alberta, Canada, was investigated at various wavelengths and resolutions. Color composite imagery consisting of wavelength bands at 2162, 2199, and 2349 nm in the red, green, and blue channels, respectively, dramatically enhanced the clarity of sedimentary features in structureless-appearing oil sand. In many cases, spectral imagery revealed features that are completely invisible to the unaided eye. In coarser-grained sections (fine to coarse sand), the enhanced contrast of sedimentological features is attributed predominantly to variability in grain size and bitumen saturation. In finer-grained sections (very fine to fine sand), enhanced contrast is mainly ascribed to variability in relative abundance of clays. A spatial resolution of at least 0.25 mm/pixel is required for imaging trace fossils, while lower resolutions (1.2–1.5 mm/pixel) are sufficient for enhancing the visibility of most sedimentary structures.

Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging

Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named Visible-Near Infrared (Vis-NIR) and another covering the 1000–2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy.

A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy

Abstract Radiative transfer models have long been used to characterize the foliar content at the leaf and canopy levels. However, they still do not apply well to close-range imaging spectroscopy, especially because directional effects are usually not taken into account. For this purpose, we introduce a physical approach to describe and simulate the variation in leaf reflectance observed at this scale. Two parameters are thus introduced to represent (1) specular reflection at the leaf surface and (2) local leaf orientation. The model, called COSINE (ClOse-range Spectral ImagiNg of lEaves), can be coupled with a directional–hemispherical reflectance model of leaf optical properties to relate the measured reflectance to the foliar content. In this study, we show that, when combining COSINE with the PROSPECT model, the overall PROCOSINE model allows for a robust submillimeter retrieval of foliar content based on numerical inversion and pseudo-bidirectional reflectance factor hyperspectral measurements. The relevance of the added parameters is first shown through a sensitivity analysis performed in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) ranges. PROCOSINE is then validated based on VNIR and SWIR hyperspectral images of various leaf species exhibiting different surface properties. Introducing these two parameters within the inversion allows us to obtain accurate maps of PROSPECT parameters, e.g., the chlorophyll content in the VNIR range, and the equivalent water thickness and leaf mass per area in the SWIR range. Through the estimation of light incident angle, the PROCOSINE inversion also provides information on leaf orientation, which is a critical parameter in vegetation remote sensing.

Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels

Aflatoxins are toxic metabolites produced by the fungi Aspergillus flavus and Aspergillus parasiticus. They can contaminate a wide range of crops before harvest and during storage. Contaminated grains are associated with economic losses for cultivators as well as potential health hazards to both humans and animals. In this study, a short-wave infrared (SWIR) hyperspectral imaging technique was utilized to detect aflatoxin contamination on corn kernels. Corn samples were inoculated with four different aflatoxin B1 (AFB1) concentrations (10, 100, 500 and 1000 μg/kg) while control samples were surface-disinfected with a PBS solution. Both infected and control samples were scanned with an SWIR hyperspectral system over the spectral range of 1100–1700 nm. A partial least squares discriminant analysis (PLS-DA) model was developed to categorize control and infected kernels and the highest overall classification accuracy yielded from the developed model was 96.9%. Spectral deviation was observed between the control and inoculated samples as the AFB1 concentrations increased. In addition, the contamination map generated with the PLS-DA model provided the visual appearance of infected samples. Our results suggest that SWIR hyperspectral imaging is a rapid, accurate, and non-destructive technique for the detection of toxic metabolites in grains and could be an alternative to manual techniques.