Milk adulteration is a significant problem globally, as it is the most widely consumed and essential food product. Due to this, monitoring milk quality is necessary for sustaining human health. A ...Machine Learning (ML) based non-destructive system was developed to identify water adulteration in milk using Near Infrared (NIR) Spectroscopy. A database was created by mixing water in milk in varying proportions (0–40 %) and capturing spectra using compact TI DLP NIR scan Nano spectroscopy in the 900–1700 nm range. The captured spectra were pre-processed with the Savitzky-Golay (SG) filter, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV) method. The most informative wavelength points were selected using the wavelength/feature selection technique, and the dimensions of these wavelengths were reduced using Principal Component Analysis (PCA). Various ML models were employed to predict the water concentration in milk. Both classification and regression methods were applied to check the system's performance. In the regression analysis, the k-Nearest Neighbour (KNN) achieved the best R2, Root Mean Square Error (RMSE), Standard Error of Prediction (SEP), Mean Absolute Error (MAE), Ratio of Performance to Deviation (RPD), Leave One Out Cross-Validation (LOOCV)-R2, and LOOCV-RMSE of 0.999, 0.399 mL ( % v/v), 0.096 mL ( % v/v), 0.227 mL ( % v/v), 33.005, 0.999, and 0.353 mL ( % v/v), respectively, while for classification analysis, the Random Forest (RF) achieved 100 % accuracy and Matthew's Correlation Coefficient (MCC). The impact of the proposed portable system, which has the potential to reshape practices and set new standards for food quality assurance, transformative, and offers solutions to critical challenges in the dairy industry.
•Efficient method proposed to predict the water adulterant in Milk Using portable NIR Spectroscopy.•Milk was adulterated with water at different adulterant levels (0–40 %).•NIR spectra captured in the 900–1700 nm range.•KNN achieved the best R2, SEP, RPD, and LOOCV-RMSE of 0.999, 0.096 ML ( % v/v), 33.005, and 0.353 ML( %v/v), respectively.•RF achieved 100 % accuracy and MCC.
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.
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•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.
Despite the importance of intramuscular fat (IMF) to eating quality, as yet no methodology has been widely adopted by the whole of industry in Australia to measure it routinely. Thus, a study was ...conducted to investigate the potential for a Near Infra-Red (NIR) device to predict the IMF content of the loin from spectra collected on the topside which is externally located on a hanging carcase and therefore easily accessible. To this end, NIR spectra were collected from topsides (m. semimembranosus) of 258 lamb carcases over 5 data collections and a sample of muscle was collected from the loin and the topside for IMF determination using a wet chemistry method. Subsequent Partial Least Square (PLS) models suggested the ability to predict the absolute IMF content of loins was poor (R2 = 0.28, RMSE = 1.26), yet there was a moderate ability to predict the IMF content of the topside (R2 = 0.56, RMSE = 0.82). Partial Least Square Discrimination Analysis (PLS-DA) models to classify cuts based on the IMF eating quality threshold of 4.5% yielded better predictive outcomes with accuracies of 66.7% and 76.7% for loin and topside respectively. However, further research to assess the relationship between the IMF of the loin and topside and reduce the impact of differences in overall absorbance between data collections will improve predictive outcomes.
•Loins were able to be classified on eating quality thresholds using data from the topside early post-mortem.•NIR spectra collected on lamb topside yielded a poor prediction of the intramuscular fat content of loins.•Prediction of the intramuscular fat content of the topside was more accurate with less error.
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.
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•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
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•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.
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.
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