Vis-NIR Hyperspectral Imaging Research Articles

Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System.

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.

Non-destructive detection of bruises in pomegranate fruit by Vis-NIR and SWIR hyperspectral imaging

Non-destructive detection of bruises in pomegranate fruit by Vis-NIR and SWIR hyperspectral imaging

Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

Vis-NIR hyperspectral imaging combined with incremental learning for open world maize seed varieties identification

There are many maize seed varieties circulating on the Chinese market, some varieties will be added and eliminated by the government every year, and fake varieties cannot be exhaustively listed. Furthermore, the updating of some varieties requires retraining the entire model, which is time-consuming and laborious. This poses a huge challenge for the authenticity identification of varieties and online model updating. Herein, hyperspectral imaging (HSI) combined with incremental learning (IL) was used to solve this problem. A novel radial basis function-biomimetic pattern recognition (RBF-BPR) model for IL was proposed and compared with one-class support vector machine (OCSVM) and BPR models under two test schemes. Hyperspectral images of five varieties of maize seeds were collected, and convolutional autoencoder (CAE) was used to extract features to remove redundant information and improve the generalization ability of models. A unique hybrid model was designed for each variety respectively, and the IL process was simulated. In two test schemes, the overall correct acceptance rate (CAR) for the known varieties and the overall correct rejection rate (CRR) for the unknown varieties of CAE-RBF-BPR model both reached 100%, which was superior to CAE-OCSVM and CAE-BPR models. Especially after RBF was used to map data, the performance of RBF-BPR model had a qualitative improvement compared with the original BPR model. In summary, the proposed method can realize IL without accessing the old classes data, while meeting the requirements of identifying known varieties and rejecting unknown varieties. In addition, if some varieties are eliminated by the government in the future, the corresponding models can also be removed form the whole system. The combination of such method and HSI has a broad application prospect in the identification of maize seed varieties, thus avoiding the trouble of retraining entire model when some maize seed varieties need to be updated.

Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling

Fungal contamination of maize during pre and post-harvest is rampant and omnipresent. Hyperspectral imaging (HSI) is a popular non-invasive technique for detection of fungal contamination in maize kernels and secures acceptance for online implementation. Designed experiments were conducted to identify the effectiveness of models (Partial Least Square- Discriminant Analysis-(PLS-DA); Artificial Neural Network (ANN); and 1D-Convolutional neural network (CNN) for predicting fungal contamination of maize kernels in relation to orientation of germ with respect to the lens of HSI (398–1003 nm) camera. Pixel wise dataset was collected for two groups of maize kernels such as sterile (G1) and Aspergillus niger contaminated (G2) and each having 100 maize kernels imaged in germ-up (GU), germ-down (GD) and germ-randomly (GR) positioned; all the three models were trained with these datasets. It was observed that in terms of “error-rate” the prediction capability was best for GU (1.31, 1D-CNN) followed by GR (1.65, ANN) and GD (1.95, ANN). In case of a mismatch of trained and testing dataset for the various models, it was observed that GR exhibited the lowest average error rates of 5.71, 4.94 and 3.15 for PLS-DA, ANN and 1D-CNN, respectively. From the results obtained in the present study confirmed that HSI along with suitable classification technique with proper germ orientation can be used to separate the infected grains.

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot-air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non-invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non-invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non-invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

Detection of frozen pork freshness by fluorescence hyperspectral image

Real-time detection of frozen meat freshness without thawing is important. This study investigates inspection of frozen pork quality attributes without thawing using fluorescence hyperspectral imaging (HSI). Partial least squares regression (PLSR) models were developed based on fluorescence spectra for total volatile basic nitrogen (TVB-N), pH, L*, a*, and b*, and compared with PLSR models based on visible/near-infrared (Vis/NIR) HSI of the same samples. Competitive adaptive reweighted sampling was used to select key fluorescence wavelengths related to each indicator. The correlation coefficients of prediction (Rp) of the models established by fluorescence spectra, with optimal pre-treatment for TVB-N, pH, L*, a*, and b*, were 0.9447, 0.9037, 0.6602, 0.8686, and 0.8699, respectively. Except for L*, fluorescence HSI-based model performance was better than that of Vis-NIR HSI. Model performance was further improved using selected key wavelengths. Results demonstrated that fluorescence HSI could determine freshness indicators of frozen pork without thawing. • Fluorescence HSI was used for the first time to assess frozen pork freshness. • Relationships between fluorescence peaks and freshness indicators were recognized. • PLSR models were compared based on fluorescence HSI and Vis/NIR HSI. • Key wavelengths were selected for each freshness indicators of frozen pork.

Real-time monitoring the color changes of large yellow croaker (Larimichthys crocea) fillets based on hyperspectral imaging empowered with artificial intelligence

Vis-NIR hyperspectral imaging (HSI) system combined with artificial neural networks was investigated for the first time to monitor color changes of large yellow croaker (Larimichthys crocea) fillets during low-temperature storage. Feed-forward neural networks (FNN) empowered with the leaky rectified linear unit (Leaky-Relu) have been developed as a non-linear quantitative analysis model. It presented accurate predictive power for color changes based on optimal spectra (with R2P of 0.908, 0.915, and 0.977; and RMSEP of 1.062, 3.315, and 0.082 for L*, a*, and b*, respectively). In final, the simplified FNN-Leaky-Relu model (S-FNN-L) was utilized to visualize the distribution maps of color parameters in the fillets. The results demonstrated the feasibility of HSI could replace the traditional colorimeter to determine the spatial distribution in the color measurement of fish fillets with a rapid and non-invasive technique.

Rapid and nondestructive prediction of firmness, soluble solids content, and pH in kiwifruit using Vis–NIR spatially resolved spectroscopy

This paper reports an evaluation of firmness, soluble solids content (SSC), and acidity (pH) in kiwifruit using a newly designed visible–near-infrared (Vis–NIR) spatially resolved spectroscopic (SRS) system. The system mainly comprises a cost-effective Vis–NIR hyperspectral imaging camera, a halogen light source, and 36 light-receiving silica fibers which were divided into six groups (1, 2, 3, 4, 5, and 10 mm away from the light illumination) used to collect diffusely reflected light from sample surface. During the experiment, time-resolved spectroscopy (TRS) was used to validate the light scattering characteristics at a single wavelength of 846 nm by transmission measurement, which differed from the reflectance measurement of the SRS system. The TRS results show that firmer kiwifruits tended to have a lower transmitted light intensity and a higher full width at half maximum value. The SRS results indicate that the reflected light intensity decreased more with an increased distance from the illumination spot in firmer kiwifruits. The results of the two methods supported the same view, i.e., firmer kiwifruit indicated higher degrees of light scattering inside. Following on, the calibration models for kiwifruit properties were constructed using the SRS data coupled with partial least squares regression analysis. Finally, the prediction accuracies were benchmarked against standard diffuse reflectance spectroscopy using one fiber group position of the same SRS system. The overall results showed the benefits of using the SRS system to predict fruit firmness by enhancing light scattering effects and predicting the SSC required for reducing such effects.

Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models.

The present study aims to estimate nitrogen (N) content in tomato (Solanum lycopersicum L.) plant leaves using optimal hyperspectral imaging data by means of computational intelligence [artificial neural networks and the differential evolution algorithm (ANN-DE), partial least squares regression (PLSR), and convolutional neural network (CNN) regression] to detect potential plant stress to nutrients at early stages. First, pots containing control and treated tomato plants were prepared; three treatments (categories or classes) consisted in the application of an overdose of 30%, 60%, and 90% nitrogen fertilizer, called N-30%, N-60%, N-90%, respectively. Tomato plant leaves were then randomly picked up before and after the application of nitrogen excess and imaged. Leaf images were captured by a hyperspectral camera, and nitrogen content was measured by laboratory ordinary destructive methods. Two approaches were studied: either using all the spectral data in the visible (Vis) and near infrared (NIR) spectral bands, or selecting only the three most effective wavelengths by an optimization algorithm. Regression coefficients (R) were 0.864±0.027 for ANN-DE, 0.837±0.027 for PLSR, and 0.875±0.026 for CNN in the first approach, over the test set. The second approach used different models for each treatment, achieving R values for all the regression methods above 0.96; however, it needs a previous classification stage of the samples in one of the three nitrogen excess classes under consideration.

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying

Hyperspectral images (400–1700 nm) of apple slices during the hot-air drying were acquired. The fusion of spectral data and Gaussian Process Regression (GPR) successfully predicted vitamin C (R-squared = 0.93 and RMSE = 0.57 mg/100 g fresh-weigh), SSC (R-squared ≈ 0.99 and RMSE ≈ 2.47%), moisture content (R-squared ≈ 1 and RMSE = 0.89%), and shrinkage (R-squared ≈ 0.98 and RMSE = 3.66% for shrinkage). The chromaticity was likewise well predicted, however, GPR models failed to predict rehydration ratio and total phenolic content. As hyperspectral systems are expensive and computationally intensive, their possible substitution with multispectral systems was investigated by finding optimal wavelengths. In this context, 1450 nm and 980 nm were singled out by using a combination of filter-based, wrapper-based, and embedded wavelength selection algorithms. The corresponding prediction accuracies for vitamin C, SSC, moisture content, and shrinkage were almost as good as those of the full spectrum. In the case of chromaticity, it is suggested to use a color camera as most of the efficient wavelengths laid in the visible range. These results indicate potential replacement of hyperspectral imaging by much simpler and lower-cost imaging sensors by which the way will be paved towards the appearance of smart dryers.

Evaluation of enhanced darkfield microscopy and hyperspectral imaging for rapid screening of TiO2 and SiO2 nanoscale particles captured on filter media.

Here we report on initial efforts to evaluate enhanced darkfield microscopy (EDFM) and light scattering Vis-NIR hyperspectral imaging (HSI) as a rapid screening tool for the offline analysis of mixed cellulose ester (MCE) filter media used to collect airborne nanoparticulate from work environments. For this study, the materials of interest were nanoscale titanium dioxide (TiO2 ) and silicon dioxide (SiO2 ; silica), chosen for their frequent use in consumer products. TiO2 and SiO2 nanoscale particles (NPs) were collected on MCE filter media and were imaged and analyzed via EDFM-HSI. When visualized by EDFM, TiO2 and SiO2 NPs were readily apparent as bright spherical structures against a dark background. Moreover, TiO2 and SiO2 NPs were identified in hyperspectral images. EDFM-HSI images and data were compared to scanning transmission electron microscopy (STEM), a NIST-traceable technique for particle size analysis, and the current gold standard for offline analysis of filter media. As expected, STEM provided more accurate sizing and morphology data when compared to EDFM-HSI, but is not ideal for rapid screening of the presence of NPs of interest since it is a costly, low-throughput technique. In this study, we demonstrate the utility of EDFM-HSI in rapidly visualizing and identifying TiO2 and SiO2 NPs on MCE filters. This screening method may prove useful in expediting time-to-knowledge compared to electron microscopy. Future work will expand this evaluation to other industrially relevant NPs, other filter media types, and real-world filter samples from occupational exposure assessments.

Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique

AbstractEffective detection of tomatoes with early decay still remains one of the major problems in the post‐harvest processing. The feasibility of using visible and near infrared (Vis–NIR) hyperspectral reflectance imaging coupled with image‐spectrum merging processing and analysis technology to detect early decay on tomatoes was observed. Mean normalization method was used to correct the uneven illumination caused by the large curvature change and the reflective characteristics of tomato surface. Principal component (PC) clustering analysis of spectra was used to find the optimal PC that was then used to distinguish sound and decayed tissues of tomatoes. Based on the selected PC, three characteristic wavelengths images at 596, 666, and 858 nm were obtained and the new combination image was calculated. The pseudo‐color image processing and RGB (red, green, and blue) transformation were applied to enhance the contrast between decayed and sound regions on the combined image. G component of RGB image and the corrected single‐wavelength image at 666 nm were used for segmentation of the decayed region and stem‐end on tomatoes, respectively. All samples including 120 decayed and 120 sound tomatoes were used to assess performance of the proposed algorithm. The classification results indicated that 100% of the decayed fruit and 97.5% of sound fruit were accurately identified by proposed algorithm. This study demonstrated that Vis–NIR hyperspectral imaging coupled with image‐spectrum merging processing technology could be a potential approach for detection of tomatoes with early decay.Practical applicationsDecay will not only affect the quality of tomato products, but also cause serious food safety problems. Detection of decayed tomatoes has become an universal attentive and urgent solving question for tomato processing industry. Currently, there was no effective and rapid technology to classify between decayed and sound tomatoes. In this study, a novel method was developed and the decayed tomatoes were successful identified using only three characteristic wavelengths images. This study can provide a useful reference for development of fast multispectral imaging system for decayed tomato detection in practical on‐line grading applications.

Quantitative analysis and visualization of moisture and anthocyanins content in purple sweet potato by Vis–NIR hyperspectral imaging

Total anthocyanin (TA) and moisture content (MC) are critical indexes of processed purple sweet potatoes (PSPs). The current study examined the feasibility of hyperspectral imaging to investigate how MC and TA contents of PSPs change during processing under two different drying methods namely convective hot-air drying (CHD) and microwave drying (MD). Models based on spectral data and the integration of spectral and one or three gray-level co-occurrence matrix features were established with partial least square regression. The best prediction outcome based on Spectra + HOMOGENEITY + CONTRAST + ENTROPY combinations were ( = 0.862, 0.847; RMSEP = 0.079, 0.303), respectively, for MC and TA for the CHD samples. Similarly, for the MD samples, the best prediction outcomes were ( = 0.867, 0.859; RMSEP = 0.088, 0.241), respectively, for MC and TA. Image algorithms were also developed to generate distribution of MC and TA in some representative samples. Practical applications Anthocyanins have been reported as having the potential to lower blood pressure, improve eyesight, reduce cancer cell multiplication, impede tumor formation, and prevent diabetes. Studies have, however, shown that cooking modes including boiling, baking, and steaming decreased the contents of anthocyanins. This study assessed the impact of convective hot-air and microwave drying (MD) process of purple sweet potato (PSP) on total anthocyanin (TA) content as a function of moisture loss. Hyperspectral Imaging (HSI) was useful in estimating not only the contents of TA and moisture, but also in generating a visual map of their distribution pattern in the processed PSPs. The TA content of PSPs processed by MD was higher than that by convective hot-air drying, but the distribution uniformity of TA in microwave dried PSPs was worse than that in convective hot-air dried samples. Finally, to guarantee consistency in drying processes of food products, the HSI could be used to obtain visual images of their chemical parameters.

Rapid and nondestructive evaluation of soluble solids content (SSC) and firmness in apple using Vis–NIR spatially resolved spectroscopy

Abstract Visible–near infrared (Vis–NIR) spectroscopy is a rapid and nondestructive method used to characterize organic compounds in postharvest fruit and vegetable assessment. However, developing robust calibration models is a challenge as conventional spectrometers collect only the cumulative effects of light absorption and scattering. In this study, a multifiber-based Vis–NIR spatially resolved spectra measurement system was designed for simultaneous evaluation of soluble solid content (SSC) and firmness in apple. Thirty silica fibers separated into five groups at 1, 2, 3, 4, and 5 mm away from the light illumination point and connected to a cost-effective Vis–NIR hyperspectral imaging camera were used to acquire spectral data with an improved signal-to-noise ratio (S/N) by a two-step signal averaging process (i.e., 30 camera pixels per fiber and six optical fibers per group). Reflectance ratio spectra were then calculated by dividing the diffusely reflected light intensity detected at distance d +△ by that detected at distance d to realize a light reference-free approach. Finally, the useful explanatory variables were selected by competitive adaptive reweighted sampling (CARS) to construct individual calibration models for various regions. The coefficients of determination ( R c a l 2 ) and the root mean square errors (RMSEcal) of the best-performing calibration models were approximately 0.97 and 0.20 % for SSC and 0.96 and 0.37 N for firmness, respectively. Furthermore, the predicted results were 0.92 and 0.35 % for SSC and 0.87 and 0.71 N for firmness. Our method offers low-cost and portable detection of SSC and firmness for postharvest fruit evaluation.

Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

The aim of this study was to investigate the potential of a micro-electromechanical NIR spectrophotometer (NIR-MEMS) and visible (Vis)/NIR hyperspectral imaging (HSI) systems to predict the moisture content, antioxidant capacity (DPPH, FRAP) and total phenolic content (TPC) of treated ground peppercorns. Partial least squares (PLS) models were developed using spectra from peppercorns treated with hot-air, microwave and cold plasma. The spectra were acquired using three spectroscopy systems: NIR-MEMS (1350–1650 nm), Vis-NIR HSI (450–950 nm) and NIR HSI (957–1664 nm). Very good predictions of TPC (RPD > 3.6) were achieved using NIR-MEMS. The performance of models developed using Vis-NIR HSI and NIR HSI were good or very good for DPPH (RPD > 3.0), FRAP (RPD >2.9) and TPC (RPD > 3.8). This study demonstrated the potential of NIR-MEMS and Vis-NIR/NIR HSI to predict the moisture content, antioxidant capacity and total phenolic content of peppercorns. The spectroscopy technologies investigated are suitable for use as in-line PAT tools to facilitate improved process control and understanding during peppercorn processing.

Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400-1000nm) hyperspectral imaging.

Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70ppb) from highly contaminated samples (200, 300 and 500ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy ( = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.

Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression–support vector machine algorithm and visible‐near infrared hyperspectral imaging

AbstractEffective classification of apples from different origins is important for horticultural processing and management in mechanized products. To obtain effective characteristic wavelengths and reduce operation time of the classification of apples produced in different areas, a method combining a variable iterative space shrinkage approach with stepwise regression (VISSA‐SR) was proposed. The whole apple sphere was chosen as the region of interest for visible‐near infrared (Vis–NIR) spectral acquisition. Savitzky–Golay smoothing, standard normalized variable, multiplicative scatter correction, and detrending were used to preprocess the spectral data, with their respective effects compared. VISSA, SR, and VISSA‐SR were applied to extract the feature wavelengths. Furthermore, support vector machine (SVM) was utilized in the construction of the identification models. The modeling results demonstrated that a VISSA‐SR‐SVM model based on detrending spectral preprocessing performed best, exhibiting calibration and prediction accuracy rates of 100% and 97.14%, and a modeling time of 131.96 s. The proposed VISSA‐SR algorithm could obtain characteristic wavelength effectively and reduce modeling process operation time. The optimal algorithm combined with Vis–NIR hyperspectral imaging could effectively non‐destructively identify apples from different regions.Practical ApplicationsIt is important to classify the apples from different regions through nondestructive testing. In order to effectively realize the rapid nondestructive testing of apples produced in different areas, the optimal algorithm combined with Vis–NIR hyperspectral imaging was adopted in this study. VISSA‐SR algorithm is proposed to obtain effective characteristic wavelength and reduce operation time. It is proved that VIS–NIR hyperspectral imaging technology is a feasible and effective method to identify apples from different regions.