Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

A new spectral simulating method based on near-infrared hyperspectral imaging for evaluation of antibiotic mycelia residues in protein feeds.

Antibiotic mycelia residues (AMRs) contain antibiotic residues. If AMRs are ingested in excess by livestock, it may cause health problems. To address the current problem of unknown pixel-scale adulteration concentration in NIR-HSI, this paper innovatively proposes a new spectral simulation method for the evaluation of AMRs in protein feeds. Four common protein feeds (soybean meal (SM), distillers dried grains with solubles (DDGS), cottonseed meal (CM), and nucleotide residue (NR)) and oxytetracycline residue (OR) were selected as study materials. The first step of the method is to simulate the spectra of pixels with different adulteration concentrations using a linear mixing model (LMM). Then, a pixel-scale OR quantitative model was developed based on the simulated pixel spectra combined with local PLS based on global PLS scores (LPLS-S) (which solves the problem of nonlinear distribution of the prediction results due to the 0%-100% content of the correction set). Finally, the model was used to quantitatively predict the OR content of each pixel in hyperspectral image. The average value of each pixel was calculated as the OR content of that sample. The implementation of this method can effectively overcome the inability of PLS-DA to achieve qualitative identification of OR in 2%-20% adulterated samples. In compared to the PLS model built by averaging the spectra over the region of interest, this method utilizes the precise information of each pixel, thereby enhancing the accuracy of the detection of adulterated samples. The results demonstrate that the combination of the method of simulated spectroscopy and LPLS-S provides a novel method for the detection and analysis of illegal feed additives by NIR-HSI.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging.

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination.

BACKGROUND: Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method. RESULTS: In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB1 and FB2 in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg-1 and 2.344 (DON), 3.714 mg kg-1 and 2.018 (FB1) and 2.104 mg kg-1 and 2.301 (FB2). Regarding classification, European Union legal limits for DON and FB1 + FB2 were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB1 + FB2 classification model. CONCLUSION: NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning.

Vigor is one of the important factors that affects rice yield and quality. Rapid and accurate detection of rice seed vigor is of great importance for rice production. In this study, near-infrared hyperspectral imaging technique and transfer learning were combined to detect rice seed vigor. Four varieties of artificial-aged rice seeds (Yongyou12, Yongyou1540, Suxiangjing100, and Longjingyou1212) were studied. Different convolutional neural network (CNN) models were built to detect the vigor of the rice seeds. Two transfer strategies, fine-tuning and MixStyle, were used to transfer knowledge among different rice varieties for vigor detection. The experimental results showed that the convolutional neural network model of Yongyou12 classified the vigor of Yongyou1540, Suxiangjing100, and Longjingyou1212 through MixStyle transfer knowledge, and the accuracy reached 90.00%, 80.33%, and 85.00% in validation sets, respectively, which was better or close to the initial modeling performances of each variety. MixStyle statistics are based on probabilistic mixed instance-level features of cross-source domain training samples. When training instances, new domains can be synthesized, which increases the domain diversity of the source domain, thereby improving the generalization ability of the trained model. This study would help rapid and accurate detection of a large varieties of crop seeds.

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation.

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.

Near-infrared hyperspectral image analysis for monitoring the cheese-ripening process.

Ripening is the most crucial process step in cheese manufacturing and constitutes multiple biochemical alterations that describe the final cheese quality and its perceived sensory attributes. The assessment of the cheese-ripening process is challenging and requires the effective analysis of a multitude of biochemical changes occurring during the process. This study monitored the biochemical and sensory attribute changes of paraffin wax-covered long-ripening hard cheeses (n = 79) during ripening by collecting samples at different stages of ripening. Near-infrared hyperspectral (NIR-HS) imaging, together with free amino acid, chemical composition, and sensory attributes, was studied to monitor the biochemical changes during the ripening process. Orthogonal projection-based multivariate calibration methods were used to characterize ripening-related and orthogonal components as well as the distribution map of chemical components. The results approve the NIR-HS imaging as a rapid tool for monitoring cheese maturity during ripening. Moreover, the pixelwise evaluation of images shows the homogeneity of cheese maturation at different stages of ripening. Among the chemical compositions, fat content and moisture are the most important variables correlating to NIR-HS images during the ripening process.

Analyzing the effects of nonthermal pretreatments on the quality of microwave vacuum dehydrated beef using terahertz time-domain spectroscopy and near-infrared hyperspectral imaging.

Both nonthermal pretreatment and nondestructive analysis are effective technologies in improving drying processes. This study evaluated the effects of different pretreatment methods on the quality of beef dehydrated by microwave vacuum drying (MVD) and compared the MVD process performance comprising real-time moisture content (MC), MC loss, colour content, and shrinkage rate using different optical sensing methods including terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI). Results indicated that osmotic pretreatment improved the drying rate of MVD beef with lower changes in colour and shrinkage rate. Both THz-TDS-based and NIR-HSI-based on-site direct scanning and in-situ in-direct sensing showed accurate prediction results, with best R2p of 0.9646 and 0.9463 for MC and R2p of 0.9817 and 0.9563 for MC loss prediction, respectively. NIR-HSI visualisation of MC results showed that ultrasound pretreatment curbed but osmotic pretreatment promoted nonuniform distribution during MVD. This research should guide improving the industrial MVD drying process.

A Self-Supervised Anomaly Detector of Fruits Based on Hyperspectral Imaging.

Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a 'spectral-spatial' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective 'spectral-spatial' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer).

Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.

Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories.

South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network.

The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the current technologies are difficult to identify colorless transparent film, which becomes an obstacle for the harvest of high-quality cotton. In this paper, an intelligent identification method is proposed to identify the colorless and transparent film on cotton, based on short-wave near-infrared hyperspectral imaging and convolutional neural network (CNN). The algorithm includes black-and-white correction of hyperspectral images, hyperspectral data dimensionality reduction, CNN model training and testing. The key technology is that the features of the hyperspectral image data are degraded by the principal component analysis (PCA) to reduce the amount of computing time. The main innovation is that the colorless and transparent film on cotton can be accurately identified through a CNN with the performance of automatic feature extraction. The experimental results show that the proposed method can greatly improve the identification precision, compared with the traditional methods. After the simulation experiment, the method proposed in this paper has a recognition rate of 98.5% for film. After field testing, the selection rate of film is as high as 96.5%, which meets the actual production needs.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm.

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000-2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g-1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network.

An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.

Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology.

The quality of wheat kernels is critical to ensure crop yields. However, in actual breeding work, unsound kernels are scarce compared to healthy kernels. Limited data sets or unbalanced data sets make it difficult for many algorithms to accurately identify kernels in different states. A novel method based on deep convolutional generative adversarial network (DCGAN) and near-infrared hyperspectral imaging technology was proposed to identify unsound wheat kernels in this paper. Three classifiers, convolutional neural network (CNN), support vector machine (SVM) and decision tree (DT) were used. After expanding the samples, the results showed that the accuracy of the test set of the DT model increased from 51.67% to 80.83%, a total increase of 29.16%. And the CNN and SVM models increased by 8.34% and 14.17% respectively. This demonstrated that the DCGAN method had the ability to generate reliable data samples for unbalanced data sets for improving the performance of the classifier. On this basis, the training samples are further expanded for improving the performance of the classifier. The results showed that CNN model gained the most from incremental data, and its accuracy rate had been continuously improved from 79.17% to 96.67%, a total increase of 17.50%. This also demonstrated that the DCGAN method had the ability to expand a limited data set. In general, the joint model based on DCGAN and CNN combined with hyperspectral imaging technology had a good prospect in the identification of unsound kernels.

Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging.

Malus micromalus Makino has great commercial and nutritional value. The regression and classification models were investigated by using near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics to improve the efficiency of non-destructive detection. The successive projections algorithm (SPA), interval random frog, and competitive adaptive reweighted sampling were employed to extract effective wavelengths sensitive to changes of soluble solid content (SSC) and firmness index (FI) information. Two types of assessment models based on full spectrum and effective wavelengths, namely partial least squares regression and extreme learning machine, were established to predict SSC and FI. In addition, the classification models based on the support vector machine improved by the grey wolf optimizer (GWO-SVM) and partial least squares discrimination analysis were constructed to differentiate maturity stage. The SPA-ELM and SPA-GWO-SVM models achieved satisfactory performance. The results illustrate that NIR-HSI is feasible for evaluation of the quality of Malus micromalus Makino.

Near-Infrared Hyperspectral Imaging as a Monitoring Tool for On-Demand Manufacturing of Inkjet-Printed Formulations.

This study evaluates the potential use of near-infrared hyperspectral imaging (NIR-HSI) for quantitative determination of the drug amount in inkjet-printed dosage forms. We chose metformin hydrochloride as a model active pharmaceutical ingredient (API) and printed it onto gelatin films using a piezoelectric inkjet printing system. An industry-ready NIR-HSI sensor combined with a motorized movable linear stage was applied for spectral acquisition. Initial API-substrate screening revealed best printing results for gelatin films with TiO2 filling. For calibration of the NIR-HSI system, escalating drug doses were printed on the substrate. After spectral pre-treatments, including standard normal variate (SNV) and Savitzky-Golay filtering for noise reduction and enhancement of spectral features, principal component analysis (PCA) and partial least squares (PLS) regression were applied to create predictive models for the quantification of independent printed metformin hydrochloride samples. It could be shown that the concentration distribution maps provided by the developed HSI models were capable of clustering and predicting the drug dose in the formulations. HSI model prediction showed significant better correlation to the reference (HPLC) compared to on-board monitoring of dispensed volume of the printer. Overall, the results emphasize the capability of NIR-HSI as a fast and non-destructive method for the quantification and quality control of the deposited API in drug-printing applications.

Beach morphodynamics and its relationship with the deposition of plastic particles: A preliminary study in southeastern Brazil.

This study describes the beach profile, characterizes microplastics and correlates their abundance with morphodynamics characteristics on three beaches from the state of São Paulo, Brazil. 745 particles were found in 4 m2 of sediment, mostly styrofoam. Nearly 90% of the fragments were found in Boracéia, the most dissipative beach, while less than 1% were found in Juréia beach, the most reflective one. The chemical composition of microplastics was identified by near-infrared hyperspectral imaging (HSI-NIR). The correlation between the abundance of particles and the slope plus the extension of the sand strip was high, as well as that found with the waves' height. These preliminary results indicate that there might be an intrinsic relation among the morphodynamical forces, the movement and destination of microplastics in marine environments.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image.

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics.

Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 µm-5 mm) after sieving. Small colorless microplastics (<600 µm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm2 scan area was probed in less than 1 min at a pixel size of 156 × 156 µm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.