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Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small thin-peel fruit, although less so for large thick-peel fruit because of the low signal-to-noise ratio of the spectral signal, resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. This research explored a method of spectroscopy for the total soluble solid (TSS) content and acidity detection of ‘Shatian’ pomelo, which are two major parameters of fruit internal flavor. VIS/NIR spectral signal detection of 100 pomelo samples during storage was performed. Detection based on raw data, signal jitter, and scattered light noise removal, feature extraction, and deep learning were performed and combined with modeling detection to achieve an accurate step-by-step detection. Our results showed that 600 W is the optimal light intensity for detecting the internal flavor of pomelo. The TSS content of pomelo is optimally detected using Savitzky-Golay (SG) + multiplicative scatter correction (MSC) + genetic algorithm (GA) + principal component analysis (PCA) + convolutional neural network (CNN) + partial least squares regression (PLSR); however, acidity of pomelo is optimally detected using SG + MSC + GA + PLSR. With the optimal detection method, the coefficient of determination and root mean squared error (RMSE) of the validation set for TSS detection are 0.72 and 0.49, respectively; and for acidity detection are 0.55 and 0.10, respectively. Even though the accuracy is not high, the data are still acceptable and helpful in nondestructive quality grading of large quantities postharvest fruit. Therefore, our results demonstrated that VIS/NIR was feasible for detecting the TSS content and acidity of postharvest pomelo, and for providing a possible method for the nondestructive internal quality detection of other large thick-peel fruit.

International audience; Drip irrigation is one of the most efficient irrigation techniques, but it is susceptible to dripper clogging. This study proposes a novel and non-destructive method based on visible and near infrared (Vis/NIR) spectroscopy coupled with chemometric methods for the discrimination and thickness estimation of physical and chemical fouling in drip-irrigation systems. Four representative materials linked to physical and chemical clogging (kaolin, bentonite, sand and calcium carbonate) at different thicknesses were selected to illustrate the potential of the approach. Partial least squares regression (PLSR) and its modification partial least squares with discriminant analysis (PLS-DA) were selected for the modelling of clogging materials. The PLS-DA model was able to predict with 96.97% accuracy all classes of materials. The PLSR models were able to estimate fouling thickness with relative prediction errors comprised between 134 μm and 164 μm. This difference appears mainly to be due to the physical properties of the selected materials. This prediction accuracy enabled the estimation of the clogging thickness between 10 and 21% of dripper channel coverage depending on the dripper channel section and the material under study. The proposed method offers an appropriate approach for clogging studies in drip irrigation systems that could be transferred to field applications.

Soil organic matter and soil particle composition play extremely important roles in soil fertility, environmental protection, and sustainable agricultural development. Visible – near-infrared reflectance (Vis–NIR) spectroscopy is a rapid, effective, and low-cost analytical method to predict soil properties. In this study, laboratory Vis–NIR spectroscopy data were used to compare the differences among partial least squares regression (PLSR), artificial neural network (ANN) and multivariate adaptive regression splines (MARSplines) based on fuzzy c-means spectral clustering and expert knowledge classification methods for soil prediction. The results showed that (1) the sand content (R2 = 0.69–0.77) had the best prediction, followed by the silt (R2 = 0.56–0.71) and organic matter (R2 = 0.54–0.69) contents, whereas the clay content (R2 = 0.29–0.65) had the poorest prediction, (2) the performance of the models followed the order of PLSR > ANN > MARSplines, and (3) the accuracies of the organic matter and sand contents were higher when applying expert knowledge classification, whereas the prediction of the clay and silt contents was better when applying spectral clustering. However, the overall accuracy of the spectral clustering method was slightly better than that of expert classification. Our findings showed that the spectral cluster-based models produced effective and interpretable prediction results for estimating soil properties. Therefore, this approach should be considered when dealing with large and heterogeneous soil samples.

Handheld near-infrared spectroscopy was used to study the effect of integration time and wavelength selection on predicting marian plum quality including soluble solids content (SSC), the potential of hydrogen ion (pH), and titratable acidity (TA). For measurements representing actual conditions, the on-tree fruits were scanned under in-field conditions. The assumption was that the robust model might be achieved when the models were developed under actual conditions. The results of the main effect test show that the integration time did not statistically affect SSC, pH, and TA predictions (p-value > 0.05) and the wavelength range had a significant impact on prediction (p-value < 0.01). An integration time of 30 ms coupled with a wavelength range of 670–1000 nm was the optimal conditions for the SSC prediction, while an integration time of 20 ms with 670–1000 nm wavelength was optimal for pH and TA prediction because of the lowest root-mean-square error of cross-validation (RMSECV). The optimal models for SSC, pH, and TA could be improved using spectral pre-processing of multiplicative scatter correction. The effective models for SSC, pH, and TA improved and reported the coefficients of determination (r2) and root-mean-square errors of prediction (RMSEP) of 0.66 and 0.86 °Brix; 0.79 and 0.15; and 0.71 and 1.91%, respectively. The SSC, pH, and TA models could be applied for quality assurance. These models benefit the orchardist for on-tree measurement before harvesting.

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.

Soluble solids content (SSC) is one of the most important quality attributes affecting the taste and maturity of fresh fruit. In this study, with the cerasus humilis fruit as the research object, a prediction model of soluble solid content (SSC) in cerasus humilis (CH) is established based on visible / near-infrared spectroscopy to explore a nondestructive testing method of the interior quality of CH. The visible / near-infrared spectral info (350-2500nm) of 160 CHs was collected to extract the reflection spectrum, establishing the linear model (PLSR) and non-linear model (LS-SVM) of CH’s spectral info and SSC. The prediction performance and stability of the model were justified using several statistical indicators namely correlation coefficient of the prediction set (Rp), the root mean square error of the prediction set (RMSEP), and the residual predictive deviation (RPD) index. Results showed that multiplicative scatter correction (MSC) was proved to be the best preprocessing method, UVE-CARS was the optimal method of dimension reduction, the quantities of characteristic wavelengths was 10 and the optimal model was UVE-CARS-PLSR, in which Rc is 0.8995, Rp is 0.8579, RMSEC is 0.8897, RMSEP is 0.9059, and RPD is 1.8766, indicating that the redundant data of the original spectrum can be reduced, the wavelength dimensions can be reduced, valid info can be retained and data processing can be simplified as UVE-CARS extracts characteristic wavelengths. Reference and theoretical basis are provided in this research for future research and development of portable detector and online sorting detection of CH internal quality.

Background To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method Hyperspectral imaging covering the spectral range of 380–1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. Conclusion We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps.

In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (Rp2) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with Rp2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries.

In this paper we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7.380 pictures. Experimental results are obtained through single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three spectral bands studied contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine scenarios studied we obtained identification rates higher or equal to 98%, when using a trained combination rule, and two cases of nine when using a fixed rule., 19 pages

The hydrous Ca‐sulfates, gypsum and bassanite, have been detected on Mars using both visible/near‐infrared (VNIR) remote sensing measurements and in‐situ rover‐based mineralogical measurements. Understanding the distributions and abundances of minerals on the Martian surface from remote sensing data is important in evaluating ancient Martian environments. Optical constants are critical input parameters in radiative transfer theory, which allows for the extraction of mineral abundances from a mixture spectrum. We have derived VNIR (0.35–2.5 μm, at 10 nm spectral sampling) optical constants of gypsum and bassanite powders using radiative transfer theory and laboratory reflectance spectra of separated size fractions as well as multiple phase angles. In addition, the MIR (2.5–25 μm, or 4,000–400 cm−1, at 2 cm−1 spectral sampling) orientation averaged optical constants of gypsum and bassanite were generated using the classical Lorentz‐Lorenz dispersion theory. We compare the optical constants of gypsum derived in this work with previously published estimates and note both similarities and differences. The optical constants derived in this work will assist in modeling VNIR reflectance spectra and quantifying mineral abundances from mineral mixtures.

Background: Forecasting bud physiologic conditions can help ‘Fuji’ apple farmers manage their orchards more efficiently. Being able to determine the nature of a bud before bud burst is one such forecast that could be of use to these ‘Fuji’ growers. The aim of this research project was to determine if a device, a visible/near-infrared spectrometer, could be employed to determine whether a bud is a flower or non-flower bud without destroying the bud. Methods: Experiments were conducted on buds taken from a ‘Fuji’ apple tree, beginning on January 29 through to March 31, 2021, three days before bud burst. The data from the visible/near-infrared spectrometer clarified whether a bud was a flower or a non-flower bud. The Spectro data Classification Learner App proved to be an accurate classification method to analyze flower and non-flower bud Spectro data. Result: Three days before bud burst, the chlorophyll content levels of the non-flower buds were markedly higher (P≤0.05) than those of the flower bud, which explains why the visible border of the near-infrared spectrometer might have been changed by the chlorophyll content of buds. The visible and near-infrared bands of the buds showed that the Spectro data of the non-flower buds were higher than those of the flower buds when measurements were made three days before bud burst. Three days before bud burst Cubic KNN of KNN classifier analyzed flower and non-flower buds smoothly. Spectro data were labeled as accuracy 75.9%, sensitivity 86% and specificity 67%. The results that were obtained suggest that farmers could use a visible/near-infrared spectrometer to identify flower and non-flower buds in their orchards, without damaging the buds, three days before bud burst.

Being able to ascertain the physiological condition of the buds on a young apple tree before bud burst could help farmers manage their orchards more efficiently, especially if they could do so without destroying the buds in the process. The experiments carried out in this study were conducted with the aim of distinguishing shoot from non-shoot buds before bud burst using a visible/near-infrared spectrometer, a device that does not destroy the buds being tested. Tests on spring-planted (April 30, 2021) trees were conducted to check shoot and non-shoot bud physiology and the winter dormancy of young ‘Jonagold’, ‘Miyabi Fuji’ and ‘Orin’ apple trees. The light absorbance of the shoot buds before bud burst was much lower than the light absorbance of the non-shoot buds as checked on the visible/near-infrared spectrometer. The highest first factor effect was determined by a PCA test conducted on shoot and non-shoot ‘Jonagold’ buds (99.9%) at a range of 640-652 nm, ‘Miyabi Fuji’ buds (99.7%) at 654-680 nm and ‘Orin’ buds (99.6%) at 704-766 nm seven days before bud burst. We also found that the highest level of accuracy, using the Classifier analysis, between shoot and non-shoot ‘Jonagold’ buds (76.6%) was one day before bud burst, for ‘Miyabi Fuji’ buds (82.1%) it was three days before and for ‘Orin’ buds (76.3%) it was two days before. These findings suggest that growers can more effectively manage the development of the young trees in their orchards with a visible/near-infrared spectrometer.

Unripe banana flour is a premium nutritious product with a potential to curb degenerative diseases through resistant starch and gluten free traits, however, with scant techniques to monitor adulteration practices. The objective of the present study was to determine the efficacy of visible–near infrared spectroscopy (Vis–NIR) spectroscopy (Vis–NIRS) in the detection and quantification of unripe banana flour adulteration with wheat flour. To do this, simulated adulteration of a composite banana flour was performed with different levels of wheat flour, in intervals of 20 g kg(−1), ranging from 0 to 800 g kg(−1). Each level was acquired in duplicate giving a total of 82 samples. Vis–NIR spectral data was acquired using a portable F-750 spectrometer in the range 447–1005 nm. Spectral data was analysed chemometrically using principle components analysis and partial least squares regression (PLSR), with 41 samples used as a calibration set and 41 for validation. The first two principal components accounted for 95% of spectral data variation, revealing five distinct clusters related to 0 g kg(−1), 20–200 g kg(−1), 220–400 g kg(−1), 420–600 g kg(−1) and 620–800 g kg(−1) adulterated samples. The best PLSR model to predict wheat flour adulteration degrees in unripe banana flour was obtained using 2nd derivative Savitzky–Golay (19-point smoothing, 2nd order polynomial), showing the highest R(c)(2) (0.991); R(p)(2) (0.993); RPD (12.021) and the lowest RMSEC (2.226 g kg(−1)) and RMSEP (1.993 g kg(−1)) values. The obtained Vis–NIRS PLSR models therefore demonstrated the technology novelty in monitoring unripe banana flour quality by the processing industries and in retail markets during product verification.

Bacterial biofilms constitute a major source of sanitary problems and economic losses in the food industry. Indeed, biofilm removal may require intense mechanical cleaning procedures or very high concentrations of disinfectants or both, which can be damaging to the environment and human health. This study assessed the efficacy of a technique based on spectroscopy in the visible, near-infrared, and short-wavelength infrared range for the quick detection of biofilms formed on polystyrene by the pathogenic bacterium Staphylococcus aureus. To do that, biofilms corresponding to three S. aureus strains, which differed in biofilm-forming ability and composition of the extracellular matrix, were allowed to develop for 5 or 24 h, representing an active formation stage and mature biofilms, respectively. Spectral analysis of the samples, corresponding to three biological replicates of each condition, was then performed by using a portable device. The results of these experiments showed that partial least-squares discriminant analysis of the spectral profile could discriminate between surfaces containing attached bacterial biomass and noninoculated ones. In this model, the two first principal components accounted for 39 and 19% of the variance and the estimated error rate stabilized after four components. Cross-validation accuracy of this assessment was 100%. This work lays the foundation for subsequent development of a spectroscopy-based protocol that allows biofilm detection on food industrial surfaces., This work was funded by the following grants: Program of Science, Technology and Innovation 2013–2017 (AGL2015-65673-R), Ministry of Economy, Industry and Competitiveness, Spain (EU Animal Health and Welfare [ANIHWA] ERA-Net; BLAAT ID: 67/PCIN-2017-001), European Regional Development Fund (ERDF) EU funds, Principado de Asturias, Spain (Proyecto Intramural CSIC 201770E016, GRUPIN14-139 and IDI/2018/000119), Ministerio de Economía y Competitividad, Spain (BIO2017-88147-R), and Programa de Apoyo y Promoción de la Investigación, University of Oviedo, Spain (PAPI 2018)

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.

Due to the multidimensional complexity and redundancy between wavelengths in the visible and near infrared (Vis-NIR) region, the speed and accuracy of data analysis can be affected. This study aims to investigate the feasibility of simplifying high dimensional data based on transformation of the spectra and local correlation maximization (LCM). These two methods will be applied to determine the prediction accuracy of air-dry density of Ulmus pumila wood. In this study, the reflectance spectra (Refl.) were subjected to the reciprocal (1/Refl.) and logarithm reflectance to improve the spectra signal for prediction. LCM was developed for selecting spectral sensitive regions that were important in the prediction of density. A local correlation coefficient (r) criterion was developed such that if the r ≥ 0.75 (between wavelength and density), then partial least squares and support vector machine (SVM) were employed as the prediction method. Likewise, 2D correlation spectroscopy plots were used to further reduce the data matrix by removing redundant wavelengths. The results showed that (1) although the sensitive region of density was different, the region of r ≥ 0.80 was mainly in the Vis and NIR spectral region. Additionally, the performance of models developed from the sensitive region was better than that of data used from the less-sensitive region. (2) The SVM model was optimized by a genetic algorithm based on the log (1/Refl.) of the sensitive region. In conclusion, it was found that the spectral transformation presented better density estimation results ( = 0.909, root mean square error of calibration = 0.014) than when less sensitive wavelengths were used in the data matrix.

As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage. The usual approach followed by producers and packinghouses to establish the internal quality and ripening of citrus fruit is to collect fruit sets throughout ripening and use them to determine the quality attributes (QA) by standard and, in many cases, destructive and time-consuming methods. However, due to the large variability within and between orchards, the number of measured fruits is seldom statistically representative of the batch, resulting in a fallible assessment of their internal QA (IQA) and a weak traceability in the citrus supply chain. Visible/near-infrared reflectance spectroscopy (Vis–NIRS) is a nondestructive method that addresses this problem, and has proved to predict many IQA of a wide number of fruit including citrus. Yet, its application on a daily basis is not straightforward, and there are still several questions to address by researchers in order to implement it routinely in the crop supply chain. This chapter reviews the application of Vis–NIRS in the assessment of the quality and ripening of citrus fruit, and makes a critical evaluation on the technique’s limiting issues that need further attention by researchers.

Albedo is an important parameter in radiation balance, energy balance, atmospheric correction of remote sensing data and weather forecasting models. The values of albedo of agricultural area vary with changes in biophysical parameters at different phenophases of the crop. Spectral and broadband albedo of visible-infrared bands for wheat canopy was estimated with numerical and spectral integration of hemispherical directional reflectance factors (HDRFs). The HDRF measurements on wheat were acquired at 54 view angles (6 azimuthal planes × 9 view zenith angles) to cover the hemispheric span up to 60° view zenith at 7-8 days’ interval during early growth stages to physiological maturity. Canopy and atmospheric parameters were measured parallel to HDRFs of the crop. The spectral and broadband albedo of wheat varies in decreasing pattern with advancement of growth stages. Visible-infrared albedo of crop was maximum at early growth stages (tillering and booting; 0.23) and minimum at late growth stages (late milking and dough; 0.15). The albedo values retrieved from HDRF in this investigation can be useful for phenophase specific applications in cropland fluxes algorithms, crop phenotyping, regional climate modeling and other remote sensing studies.

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

mail janv/fev; International audience; Although the main prehistoric color used for paintings is red, knowledge of this coloring matter often boils down to saying that it is “ochre.” However, the red coloring matter of Prehistory is numerous and may have been the subject of various preparations, mixtures, or even alterations. Understanding the use and transformation of coloring matter raises questions about the technical processes but also about the supply strategies of these ancient societies. In the case of analysis of solid archaeological remains, we can access the petrography, mineralogy and chemistry of these ferruginous rocks. But, when it is about deposited powder, the means of investigation become limited. We therefore propose to test the complementarity of spectro-radiometry, a non-invasive method that allows us to obtain a spectral signature of the material whatever its mode of preparation. From six geological reference samples chosen for their color (from red to yellow) and for their mineralogical composition, spectra in the visible and near-infrared were recorded under several experimental conditions and several modes of preparation of the matter, using two spectro-gonio radiometers. It is then possible to discriminate these different coloring matter on the basis of their spectral signature and to understand the link with their mineral composition.

Data about diet and digestion process of cattle are important for the fine-tuning of the diet and from an environmental point of view. Given the capacity of the near-infrared reflectance spectroscopy (NIRS) to provide easily, quickly and cheap data its ability in predicting dietary and faecal chemical composition, fibre-bound N and total-tract apparent digestibility (ttaD) of beef cattle were tested. The ttaD was estimated using the dietary and faecal undigestible neutral detergent fibre (uNDF) as an internal marker. A total of 172 pool faecal samples and 164 total mixed ration (TMR) samples were randomly collected 24 h post-feeding across the fattening groups of young males and females Charolaise beef cattle. Both TMR and faeces were analysed chemically and through visible/NIRS instrument. Calibration models were developed using a modified partial least squares (mPLS) regression analysis and tested by a leave-one-out cross-validation procedure and the best calibrations were selected based on various parameters including the coefficient of determination of calibration (R2CrV) and the residual predictive deviation (RPD). The overall composition of TMR and faeces were similar to that reported in literature and the coefficient of variation was higher than 12% for most of the parameters studied. The NIRS was able to accurately predict the ADF, nitrogen (N), and ash content in the TMR, whereas in faeces only the ADF prediction was acceptable. The ttaD and total-tract true digestibility of N using the uNDF as an internal marker were inaccurately predicted both in TMR and in faeces (R2CrV ≤0.66; RPD ≤ 1.71).Highlights Near-infrared spectroscopy was not a suitable technology to predict total tract apparent digestibility. NIRS was able to accurately predict the ADF, nitrogen and ash content in the TMR. NIRS was able to accurately predict the ADF in faeces.

BackgroundExisting diagnostic methods for the parasitic gastrointestinal nematode,Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces.MethodsVisible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores.ResultsPrincipal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood.ConclusionThis study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas whereH. contortusis endemic.Graphical Abstract

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400&ndash, 2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents&rsquo, specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models&rsquo, identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

The early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai‘i, two recently described fungal species are responsible for increasingly widespread mortality in ‘ōhi‘a Metrosideros polymorpha, a foundational tree species in Hawaiian native forests. In this study, we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can detect pre-symptomatic trees infected with these pathogens. After generating a dense time series of laboratory spectral reflectance data and red green blue (RGB) images for inoculated ‘ōhi‘a seedlings, seedlings subjected to extreme drought, and control plants, we found few obvious spectral indicators that could be used for reliable pre-symptomatic detection in the inoculated seedlings, which quickly experienced complete and total wilting following stress onset. In the field, we found similar results when we collected repeat multispectral and RGB imagery over inoculated mature trees (sudden onset of symptoms with little advance warning). We found selected vegetation indices to be reliable indicators for detecting non-specific stress in ‘ōhi‘a trees, but never providing more than five days prior warning relative to visual detection in the laboratory trials. Finally, we generated a sequence of linear support vector machine classification models from the laboratory data at time steps ranging from pre-treatment to late-stage stress. Overall classification accuracies increased with stress stage maturity, but poor model performance prior to stress onset and the sudden onset of symptoms in infected trees suggest that early detection of rapid ‘ōhi‘a death over timescales helpful for land managers remains a challenge.

This paper presents a smart machine learning system for classification of hyperspectral wheat data based on deep learning methodology. For this purpose, the performances of AlexNet and VGG16 models were investigated for the classification of hyperspectral wheat samples. In this study, the Support Vector Machine (SVM) and Softmax classifiers were carried out to predict labels of wheat kernels. In order to evaluate the system performance, a new hyperspectral wheat test dataset was constructed using Visible-Near Infrared images (VNIR) including 50 wheat species with 220 images per specimen, as 11000 samples in total. With experiments applied on newly created test dataset, overall approximated accuracy rates of 96.00% and 99.00% determined by linear SVM classifier, in case of fully connected layer (FC6 and FC7) features for AlexNet and VGG16, respectively. From the Softmax predictions, the 92% and 70% of samples were correctly discriminated based on trained VGG16 and AlexNet models, respectively. The obtained superior results show that using a deep Convolutional Neural Networks (CNN) architecture is more efficient by the means of accurate discrimination of wheat species. The proposed deep learning based categorization system promises high accuracy results for the quality analysis, classification and disease detection in food., Bu makale, derin öğrenme metodolojisine dayalı hiperspektral buğday verilerinin sınıflandırılması için akıllı bir makine öğrenme sistemi sunmaktadır. Bu amaçla, hiperspektral buğday örneklerinin sınıflandırılması için AlexNet ve VGG16 modellerinin performansları araştırılmıştır. Bu çalışmada, buğday çekirdeklerinin türlerini tahmin etmek için Destek Vektör Makinesi (DVM) ve Softmax sınıflandırıcıları kullanılmıştır. Sistem performansını değerlendirmek için, Görünür Yakın Kızılötesi Görüntüleme (VNIR) kullanılarak 50 buğday türüne ait tür başına 220 görüntü toplamda 11000 örnek içeren yeni bir hiperspektral buğday test veri kümesi oluşturulmuştur. Yeni oluşturulan test veri seti üzerinde yapılan deneylerde, AlexNet ve VGG16 için tamamen bağlı katman (FC6 ve FC7) özellikleri kullanılması durumunda doğrusal DVM sınıflandırıcısı tarafından belirlenen yaklaşık %96.00 ve % 99.00'lık genel doğruluk oranları elde edilmiştir. Softmax sınıflandırıcı ile numunelerin sırasıyla %92 ve %70'i, eğitimli VGG16 ve AlexNet modellerine göre doğru bir şekilde ayırt edilebilmiştir. Elde edilen üstün sonuçlar, derin bir Evrişimsel Sinir Ağları (ESA) mimarisi kullanmanın, buğday türlerinin doğru bir şekilde ayırt edilmesi yoluyla daha verimli olduğunu göstermektedir. Önerilen derin öğrenme temelli sınıflandırma sistemi, gıdalarda kalite analizi, sınıflandırma ve hastalık tespiti için yüksek doğrulukta sonuçlar vaat etmektedir.

The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky&ndash, Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.

Metal bis(1,2-dithiolene) complexes belonging to the class [Ni(Ar-edt)2]x- [Ar-edt2- = arylethylene-1,2-dithiolate; Ar = phenyl, (1x-), 2-naphthyl (2x-); x = 0 and 1] were fully characterized by NMR, UV-visible-near-infrared (UV-vis-NIR), diffuse reflectance, and FT-IR spectroscopy, as well as cyclic voltammetry and single-crystal X-ray diffraction analysis. These complexes have emerged as new photoconducting materials that allowed for the development of a prototype of photodetectors with response in the vis-NIR region. The photodetecting devices showed in some cases quantum efficiencies orders of magnitude higher than those of previously reported 1,2-dithiolene systems.

The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near-infrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.

Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques&mdash, in this case, laser-induced breakdown spectroscopy (LIBS)&mdash, and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.

Lanthanide(III) ions (Ln3+) in coordination compounds exhibit unique luminescence properties with narrow and characteristic f-f transitions throughout the visible and near-infrared (NIR) ranges. In...

With increasing consumer interest in healthy food formulations made from pulse flours, the research gap in characterizing them based on their pulse type and milling technique has come to limelight. To this end, the feasibility of using hyperspectral imaging in the visible near infrared (Vis-NIR) (400–1000 nm) and short wave infrared (SWIR) (1000–2500 nm) regions to classify pulse flours (viz. chickpea, yellow pea, navy bean and green lentil) based on the pulse type and milling methods was investigated. Unsupervised and supervised classification models were developed using unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA). Supervised classification for pulse flour type demonstrated 100% accuracy in the Vis-NIR wavelength range of 530–700 nm, which primarily exploited the color attributes of the flour samples. For milling method based classification, PLS-DA models developed using SWIR regions of 1370–1500 nm and 1700–2000 nm played a significant role in discriminating flour samples yielding 95% classification accuracy. These regions are associated with the O–H and N–H overtones of the proteins found in flour samples. Conclusively, hyperspectral imaging in the range of 400–2500 nm combined with multivariate data classification methods can reliably be used by the food industry for characterization of pulse flours.

A new family of [3.3.1] metallacryptates with the general composition [LnGa6 (H2 shi)(Hshi)(shi)7 (C5 H5 N)] (Ln-1; shi(3-) =salicylhydroximate; Ln = Pr, Nd, Sm-Yb) has been synthesized and characterized. Ln-1 display both interesting magnetic and luminescent properties. Sm-1 has sharp emission bands in the visible and the near-infrared (NIR) regions with quantum yield values (QSmL ) of 1.64(9) and 5.5(2)(.) 10(-2) %, respectively. Tb-1 exhibits a weak green emission (QTbL =1.89(3)(.) 10(-1) %) while Pr-1, Nd-1, Ho-1, Er-1, and Yb-1 possess emission bands in the NIR range with QPrL =3.7(2)(.) 10(-3) %, QNdL =1.71(5)(.) 10(-1) %, QHoL =1.1(2)(.) 10(-3) %, QErL =7.1(2)(.) 10(-3) % and QYbL =0.65(3) %. Nd-1, Dy-1, and Yb-1 display slow magnetization relaxation in an applied field, where only Dy-1 has been observed to follow an Orbach process (Ueff =12.7 K). The combination of NIR emission with magnetic properties makes Nd-1 and Yb-1 attractive candidates as smart materials addressable in two manners.

Recent developments and applications of rapid measurement tools (RMTs) such as visible near-infrared (vis-NIR) spectroscopy confirmed that these technologies can provide 'fit for purpose' and cost-effective data for risk assessment and management of oil-contaminated sites. Although vis-NIR spectroscopy has been used frequently to predict total petroleum hydrocarbons (TPHs), it has had limited use for polycyclic aromatic hydrocarbons (PAHs) and there has been none for alkanes. In the present study, the potential of vis-NIR spectroscopy (350-2500 nm) to measure PAHs and alkanes in 85 fresh (wet, unprocessed) oil-contaminated soil samples collected from three sites in the Niger Delta, Nigeria, was evaluated. The vis-NIR signal and alkanes and PAHs measured in the laboratory by sequential ultrasonic solvent extraction followed by gas chromatography-mass spectrometry (GC-MS) were then used to develop calibration models using partial least squares regression (PLSR) and random forest (RF) modelling tools. Prior to model development, the pre-processed spectra were divided into calibration (75%) and prediction (25%) sets. Results showed that the prediction performance of RF calibration models for both alkanes (a coefficient of determination (R-2) of 0.58, a root mean square error of prediction (RMSEP) of 53.95 mg kg(-1) and a residual prediction deviation (RPD) of 1.59) and PAHs (R-2 = 0.71, RMSEP= 0.99 mg kg(-1) and RPD=1.99) outperformed PLSR (R-2 = 0.36, RMSEP=66.66 mg kg(-1) and RPD=1.29, and R-2 = 0.56, RMSEP=1.21 mg kg(-1) and RPD=1.55, respectively). The RF modelling approach accounted for nonlinearity of the soil spectral responses and therefore resulted in considerably greater prediction accuracy than the linear PLSR. Adoption of vis-NIR spectroscopy coupled with RF is recommended for rapid and cost-effective assessment of PAHs and alkanes in contaminated soil.

Nowadays, monitoring and observing insect pest populations is a major and crucial issue in agriculture, especially for crop protection. Spectroscopic techniques are well recognized for dete...

Fungal diseases are among the most common problems that affect tomato fruits, especially after harvesting. The early detection of these diseases may save 60% of the losses. Thus, this study aimed to detect the gray mold disease at an early stage on tomato fruits using the non-destructive visible/near-infrared (VIS/NIR) technique. Botrytis. cinerea fungus was isolated and identified using two specific primer sets (C729+/- and BC108+/BC563-). Three tomato varieties were used for this study (i.e. Harver, Izmer and Ekram), 30 samples from each variety, 20 samples were injected with the B. cinerea and 10 samples were left as control/healthy samples. Samples were examined using the VIS/NIR spectroscopy with a range of 550-1100 nm for three days. Thereafter, the data were analyzed using principal component analysis (PCA), where the results showed the ability of VIS/NIR to detect the infected samples on the second day of the injection, and before the symptoms appear on the samples. Two PCs explained 99% and 100% of VIS/NIR variance, respectively.

Visible-near-infrared (VIS-NIR) spectroscopy is a fast and non-destructive method for analyzing materials. However, most commercial VIS-NIR spectrometers are inappropriate for use in various locations such as in homes or offices because of their size and cost. In this paper, we classified eight food powders using a portable VIS-NIR spectrometer with a wavelength range of 450–1,000 nm. We developed three machine learning models using the spectral data for the eight food powders. The proposed three machine learning models (random forest, k-nearest neighbors, and support vector machine) achieved an accuracy of 87%, 98%, and 100%, respectively. Our experimental results showed that the support vector machine model is the most suitable for classifying non-linear spectral data. We demonstrated the potential of material analysis using a portable VIS-NIR spectrometer.