Analytical tests are commonly performed in laboratories to analyze and ensure food quality due to concerns about food adulteration. However, traditional analytical methods that rely on chemicals or ...equipment are often time-consuming and expensive. Therefore, we propose an efficient method for detecting starch adulterants in turmeric, which is clean, green, inexpensive, and rapid. Near-infrared (NIR) spectroscopy meets all these criteria and has a high potential for conducting routine assessments. To acquire reflectance spectra from the 900 nm–1700 nm range, we used the compact TI DLPNIRscan Nano module instead of a traditional bulky and costly spectrophotometer. Turmeric samples were adulterated with starch, ranging from 0% to 50%, and the Savitzky–Golay (SG) filter was applied to the recorded spectra. Various machine learning (ML) models were used to train and test these spectra, and the PCA approach was used to reduce the dimensionality of the data and assess its effectiveness. We have used several metrics, including R2, Root Mean Square Error (RMSEV), Mean Absolute Error (MAEV), and Leave-one-out Cross-Validation (LOOCV), to evaluate the performance of the ML models. The Extra Tree Regressor (ETR) outperformed the other models, achieving an R2 of 0.995, an RMSEV of 1.056 mg (W/W), an MAE of 0.597 mg (W/W), a LOOCV R2 of 0.994, and a LOOCV RMSEV of 1.038 mg (W/W).
•DLP NIRscan spectrometer (900–1700 nm).•Starch detection in turmeric.•Performance confirmed with repeatability and stability tests.
Rice is the second most important food staple worldwide and the demand will continue to increase with the growth of the world population. As reports grow that frauds is prevalent in many supply ...chains there is the need for an effective and rapid technique for monitoring the authenticity and quality of rice. This study investigated the novel application of hand-held NIR spectrometry coupled to chemometric for the estimation of rice authenticity and quality in real time. A total of 520 rice samples from different quality grades (high quality, mid quality and low quality) and different countries (Ghana, Thailand, and Vietnam) of origin were used. Among the pre-processing methods used multiplicative scatter correction (MSC) was found to be superior. Principal component analysis (PCA) was used to extract relevant information from the spectral data set and the results showed that rice samples of different categories could be clearly clustered under the first three PCs using the MSC preprocessing method. The performance of K-nearest neighbor (KNN) revealed that for authentication of rice quality grades, the classification rate gave 91.62% and 91.81% in training set and prediction set respectively while identification rate based on different country of origin was 90.84% and 90.64% in both training set and prediction set respectively. For the differentiation of local rice from the imported, KNN and SVM all had 100% in both the training set and prediction set. These gives very strong evidence that hand-held spectrometry coupled with MSC-PCA-KNN could successfully be used to provide rapid and nondestructive classification of rice samples according to different quality grades, geographical origin and imported versus locally produced rice. This technique could enhance the work of quality control inspectors both from industry and regulatory perspectives for the rapid detection of rice integrity and fraud issues.
Display omitted
•MSC-PCA preprocessing gave a clear cluster trend that showed the distinct quality groupings•Optimal classification models were achieved by MSC+PCA+ KNN method•Hand-held spectrometer and chemometrics method was used to identify rice quality and authenticity
Display omitted
•A blue-LED-excitable NIR-II luminescence is revealed in MgO:Cr3+,Ni2+ phosphor.•Performance: QY = 92.7 %, FWHM = 235 nm, thermostable (83.0 %@150 ℃).•The efficient NIR-II pc-LED ...device (27.4 mW at 350 mA) is fabricated.•The application potential of this NIR-II light source is demonstrated.
The development of phosphor-converted light-emitting diodes (pc-LEDs) in the second near-infrared window (NIR-II, 1000–1700 nm) represents an important, newly emerging, and dynamic field in NIR spectroscopy. Unfortunately, lacking efficient NIR-II phosphors that can be excited by commercial blue LED chips impedes NIR spectroscopy applications. Herein, a donor–acceptor strategy is developed by introducing Cr3+ sensitizers to greatly enhance the blue-light excitation efficiency of NIR-II-emitting MgO:Cr3+,Ni2+ phosphor. Consequently, a high-efficiency blue-light-excitable MgO:Cr3+,Ni2+ phosphor that gives a NIR-II emission at 1335 nm with a full width at half-maximum (FWHM) of 235 nm, is demonstrated. The product has a high internal quantum efficiency of 92.7 % and excellent thermal stability, maintaining 83.0 % of the room temperature emission intensity at 150 °C. Excitingly, the fabricated NIR-II pc-LED device shows a high NIR-II optical power (27.4 mW@350 mA). The performances of the achieved NIR-II pc-LED are almost the best results until now. Additionally, multifunctional applications including nondestructive detection and anti-counterfeiting of the NIR-II light source are demonstrated. These results are pretty crucial for the further development of NIR-II spectroscopy and imaging technology.
Display omitted
•NIR analysis of water for evaluating its recycling for agricultural cultivation.•To design a deep learning CNN architecture for NIR model optimization.•Decision tree employed for CNN ...pooling as smart feature extraction strategy.•To realize intelligent parameter optimization by a shallow CNN architecture.
Water is a natural resource for agricultural irrigation. Recycling use of water is important in terms of resource conservation and is good for sustainable development of the ecological environment. The wastewater from daily living and industrial production contains various chemicals that are supposed as pollutants leading to the decline of water quality. For the demand of water protection and recycling, the assessment of water pollution level should be evaluated. An effective scientific technique is required for rapid detection of water pollution. Near-infrared (NIR) spectroscopy is a modern technology suitable for rapid detection of agricultural targets. For monitoring the agricultural water resource, the NIR modeling methods are required to be smart and artificially controlled to solve the issues when we confront a considerable number of data or a dynamic situation. In this study, an improved convolutional neural network (CNN) architecture was designed for a deep calibration on the NIR data. The architecture is shallow, simply constructed with one convolution layer and one pooling layer. The decision tree algorithm was employed in the pooling layer for extracting the informative features in a data driven manner. The CNN architecture was trained by combined tuning of multiple parameters in different layers. The convolution filters, the decision tree branches and the hidden neurons in the fully connected layer were automatically adaptive with fidelity to the measured data. A CNN calibration model for NIR quantitatively determination of water pollution level was then established and optimized in deep learning mode, and eventually improved the NIR prediction accuracy. Prospectively, the designed shallow CNN architecture is feasible to be used for establishing intelligent spectroscopic models for evaluating the level of water pollution, and is expected to provide smart technical support in dealing with the issues of water recycling and conservation for agricultural cultivation.
Near infrared (NIR) spectroscopy represents an emerging analytical technique, which is enjoying increasing popularity in the food processing industry due to its low running costs, and since it does ...not require sample preparation. Moreover, it is a non-destructive, environmental friendly, rapid technique capable for on-line application. Therefore, this technique is predestined for implementation as an analytical tool in industrial processing. The different fields of application of NIR spectroscopy reported in the present review highlight its enormous versatility. Quantitative analyses of chemical constituents using this methodology are widespread. Moreover, a wide range of qualitative determinations, e.g. for authenticity control, sample discrimination, the assessment of sensory, rheological or technological properties, and physical attributes have been reported. Both animal- and plant-derived foodstuffs have been evaluated in this context. Highly diverse matrices such as intact solid samples, free-flowing solids, pasty, and fluid samples can by analysed by NIR spectroscopy. Sophisticated conditions for the application in industrial scale comprise among others measurements on moving conveyor belts, in continuous flows in tubes, and monitoring of fermentation processes. For such purposes, different construction designs of NIR spectrometers for hyperspectral imaging, portable devices, fibre optical and direct contact probes as well as tube integrated probes measuring through windows, and automated sample cell loading have been developed. In the present review, emphasis was put on studies dealing with on-line application of NIR spectroscopy for industrial processes in the food industry, which were categorised according to their application conditions into semi-industrial scale and industrial scale.
•Near infrared (NIR) spectroscopy enjoys increasing popularity in food science.•Presentation of general aspects of NIR spectroscopy in food analysis.•Versatility of applications is shown.•On-line applications are discussed.•Laboratory, semi-industrial, and industrial applications are classified.
•On-line visible and near infrared spectroscopy is a good tool for mapping P variability.•Heterogeneity in P distribution can be reduced by site specific application of P2O5.•On-line vis–NIR sensor ...is a good tool to manage P in the field.
Current methods of phosphorous (P) management based on conventional soil sampling of one sample per ha followed by laboratory analysis are tedious, time consuming, expensive and does not allow exploring spatial variation in P at a desired fine spatial scale. Visible and near infrared (vis–NIR) spectroscopy has proven to be a robust, quick and relatively cost effective tool to measure key soil properties with appreciable accuracy. This paper aims at utilising high spatial resolution P data generated with an on-line vis–NIR spectroscopy sensor for site specific management of P2O5 fertiliser for enhanced uniformity of P spatial distribution across the field, which is hoped to optimise and homogenise crop growth and yield. On-line measurement was carried out for three successive years of 2011, 2012 and 2013 after crop harvest in a 21ha field in Duck end farm, Bedfordshire, the UK. Variable rate (VR) P was only applied in year 2 after crop harvest, where the field was divided into 4 P-index zones. Indexes 0 and 1 received 140kgha−1 and 70kgha−1 P2O5, respectively, whereas indexes 2 and 3 received no P2O5 fertiliser. The purpose of this VR P application was to attempt unifying the entire field to index 2, which is considered the optimal P level for cereal crops.
showed that the on-line measurement accuracy of P was acceptable with coefficient of determination (R2), root mean square error of prediction (RMSEP) and residual prediction deviation (RPD) of 0.60, 0.60mg100g−1 and 1.5, respectively. However, accuracy was larger with soil samples scanned under laboratory non-mobile conditions with R2, RMSEP and RPD of 0.75, 0.51mg100g−1 and 1.8, respectively. The VR application of P2O5 after crop harvest in year 2 improved the uniformity of the spatial distribution of P, measured in year 3 with the on-line soil sensor. The number of zones of P-index was decreased from 4 indexes before P2O5 VR application to a uniform P index e.g. index 2. The coefficient of variation (CV) of P in the field was reduced from 26% in 2011, and 25% in 2012, to 16% in 2013. The on-line measured P map of year 3 showed significant improvement in the uniformity of P spatial distribution across the field, comparing to previous years. It was concluded that the on-line vis–NIR soil sensor is an effective tool to manage and minimise within field variation in P in arable crops. However, a further study is needed that should include more fields with different soil types in order to generalise the results achieved in the current work.
It is widely known that the visible and near infrared (VIS-NIR) spectroscopy has the potential of estimating soil total nitrogen (TN), organic carbon (OC) and moisture content (MC) due to the direct ...spectral responses these properties have in the near infrared (NIR) region. However, improving the prediction accuracy requires advanced modelling techniques, particularly when measurement is planned for fresh (wet and un-processed) soil samples. The aim of this work is to compare the predictive performance of two linear multivariate and two machine learning methods for TN, OC and MC. The two multivariate methods investigated included principal component regression (PCR) and partial least squares regression (PLSR), whereas the machine learning methods included least squares support vector machines (LS-SVM), and Cubist. A mobile, fibre type, VIS-NIR spectrophotometer was utilised to collect soil spectra (305–2200 nm) in diffuse reflectance mode from 140 wet soil samples collected from one field in Germany. The results indicate that machine learning methods are capable of tackling non-linear problems in the dataset. LS-SVMs and the Cubist method out-performed the linear multivariate methods for the prediction of all three soil properties studied. LS-SVM provided the best prediction for MC (root mean square error of prediction (RMSEP) = 0.457% and residual prediction deviation (RPD) = 2.24) and OC (RMSEP = 0.062% and RPD = 2.20), whereas the Cubist method provided the best prediction for TN (RMSEP = 0.071 and RPD = 1.96).
•LS-SVM, Cubist, PCR and PLSR were used for VIS-NIR prediction of TN, OC, and MC.•MC and OC are best predicted by LS-SVM and TN is best predicted by the Cubist.•Machine learning methods perform better than the multivariate regression methods.
The potato size and shape can affect the accuracy of predicting potato quality using near-infrared (NIR) spectroscopy. This study used NIR spectroscopy and a linear-nonlinear algorithm to eliminate ...the influence of potato size and shape on the accuracy of the prediction model for potato starch and moisture content. Savitzky-Golay (SG) filtering and four dimensionality reduction algorithms (iterative variable subset optimization (IVSO), variable combination population analysis- iteratively retaining informative variables (VCPA-IRIV), bootstrapping soft shrinkage (BOSS), and principal component analysis (PCA)) were used to optimize the NIR spectrum and extract spectral data. Partial least squares (PLS) linear regression and a nonlinear model (convolutional neural network–bi-directional long short-term memory (CNN-BiLSTM)) were used to establish and compare 52 quantitative prediction models. The optimum prediction model was the SG-IVSO-PLS-CNN-BiLSTM. Its correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and relative percent deviation (RPD) were 0.949, 1.350 %, and 3.172 for predicting the moisture content and 0.937, 1.110 %, and 2.863 for predicting the starch content. The SG-IVSO-PLS-CNN-BiLSTM eliminated the influence of potato size and shape on the accuracy of the prediction model. This method is suitable for predicting potato quality in the potato processing industry.
Display omitted
•Non-destructive testing of potatoes with a large size range.•IVSO, BOSS, and VCPA-IRIV algorithms optimize the performance of prediction models.•Linear and nonlinear methods are combined and compared.•Combined models can be used to detect starch and moisture.
Fritillaria is a popular Chinese medicinal crop known for its medicinal value. However, the chemical composition and medicinal value of Fritillaria can vary significantly depending on the variety and ...origin. Therefore, there is a need for a more efficient and accurate method to identify Fritillaria of different varieties and origins. This study employed a portable near-infrared (NIR) spectrometer to collect NIR spectral data on six kinds of Fritillaria and classify them by using different feature extraction methods. In order to circumvent the limitations of Linear Discriminant Analysis (LDA) in small-sample size problem and improve the classification accuracy, Fuzzy Generalized Singular Value Decomposition (FGSVD) was proposed for feature extraction from NIR spectra. The original NIR data were first pre-processed by the Savitzky-Golay (SG) filtering, and then their dimensionality was reduced by Principal Component Analysis (PCA). After that, Generalized Singular Value Decomposition (GSVD) and FGSVD were performed for feature extraction, respectively. In the end, the spectral data were classified into six categories using the k-Nearest Neighbour (KNN) classifier and Extreme Learning Machines (ELM). The results of the study showed that FGSVD had the highest classification accuracy when using the KNN classifier and was able to achieve 100 % classification accuracy. At the same time, GSVD and LDA were able to achieve 97.5 % and 92.5 % classification accuracy respectively. The classification accuracy obtained by FGSVD was also higher than GSVD and LDA when ELM was used. The highest classification accuracies obtained by FGSVD, GSVD and LDA were 96.9 %, 94.2 % and 92.3 % respectively. Thus, the KNN classifier outperformed ELM in classification. Consequently, the portable near-infrared spectrometer coupled with PCA+FGSVD is a novel strategy for rapid and non-destructive classification of Fritillaria.
•Proposed fuzzy generalized singular value decomposition effectively extracts discriminant features of NIR spectra.•Fritillaria variety detection using NIR spectroscopy.•PCA+FGSVD achieves the highest classification accuracy.•Provided a strategy for rapid and non-destructive classification of other agricultural products.
PDF
