Search

Background Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). Results A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice. Conclusion These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice. © 2019 Society of Chemical Industry.

Visible and Near Infra-Red Spectroscopy (Vis/NIRS) technique was investigated as a non-destructive optical method in order to authenticate argan oil. This study focuses on the detection and quantification of argan oil adulteration with cheap vegetable oils, using Vis/NIRS associated with chemometric tools in the spectral ranges from 500 to 1000 nm and 1000 to 1700 nm. The results achieved using baseline and first derivative pretreatments before analyzing data set with multivariate calibration methods are the best. Partial Least Squares (PLS) model has been established to predict the concentration of adulterants in pure argan oil with a correlation of 0.90, Root Mean Square Error of Prediction (RMSEP) of 4.67, Standard Error of Prediction (SEP) of 4.57 and a bias of 1.26. Principal Component Analysis (PCA) model was established to discriminate samples according to the type of adulterants. The results show Vis/NIRS efficiency as a rapid and non-destructive method and the importance of chemometric tools in order to develop accurate models to authenticate argan oil. Less than 1 s run by NIRS allows the identification of adulterants in pure argan oil from a percentage of adulteration of 0.35% without needing to prepare or destruct samples.

Salmonella is an organism of importance to the poultry industry with increasingly stringent government regulatory standards. Real-time polymerase chain reaction (RT-PCR) and plating procedures on nutrient enriched growth media have been the standard detection methods of Salmonella from broiler chicken carcasses for years. These methods are proven, but offer disadvantages in the amount of time or reoccurring sample cost. Here, we propose the use of a hyperspectral microscope imaging system (HMI) for comparison to standard detection methods. Broiler chicken carcasses were rinsed and plated on Salmonella selective agar. Colonies from plates were picked and RT-PCR was used as a confirmation test to verify plating results, while HMI was collected from the same colonies. Spectral signatures of cells were extracted between 450 and 800 nm from HMI collected with 100x objective. A quadratic discriminant analysis (QDA) was used to classify cells as either Salmonella positive or negative (n = 341). Spectra preprocessing minimized the influence of cellular shape on the spectra, increasing the initial classification accuracy of 81.8–98.5%, yielding a sensitivity of 1.0, and a specificity of 0.963. Results showed the potential as an initial investigation of HMI as a microbial confirmation tool, compared to RT-PCR.

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.

Identification of unfertilized duck eggs before hatching was hardly achieved in the poultry farm by the traditional method. Visible/near infrared (VIS/NIR) transmittance spectroscopy was used as a rapid and non-destructive technique to distinguish the unfertilized duck eggs before hatching. Transmittance spectral data of duck eggs laid by the same flock of ducks was obtained. Prediction models were developed and used to link the non-destructive assessment methods and qualitative observation values of spectral with the spectral data. Established models were validated by the internal leave-one-out cross-validation (LOOCV) and external validation. The prediction model established by standard normal variate (SNV) pre-processing method and naive Bayes (NB) modeling method got the best performance. The overall prediction accuracy of the validation set was 94.54% and the prediction accuracy of internal validation was 95.06%. The prediction accuracy of 667 duck eggs by means of NB modeling method by SNV pre-processing was 94.77%. Results showed that the VIS/NIR transmittance spectroscopy was an effective tool for detecting the unfertilized duck eggs. This provided technical support for identification of the unfertilized duck eggs.

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.

Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all >0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.

Indium-gallium-zinc oxide- and zinc oxynitride-based heterojunction phototransistors were successfully demonstrated to control the persistent photoconduction (PPC) effect and be also responded sensitively at the range from visible to near-infrared. ZnON plays a key role in extending the spectral response at various frequencies of operation. The devices show significantly different photoresponse and photorecovery characteristics depending on the number of stacked layers of IGZO and ZnON. After negative bias and illumination stress was applied to the devices for 1 h, tandem-structure-based phototransistors recovered remarkably better than single-component IGZO devices. We suggest that the improvements to photoresponse and photorecovery result from the presence of potential wells between two IGZO layers and the energy band alignment of the tandem structure.

Edible oil forgery is a prominent issue of food production bussiness. In order to lower the production cost and increase the apparent value, some cheap substance is added on the edible oil production and it was mislabelled. Current techniques are able to differentiate each type of edible oils based on its physical characteristics. However, they need special treatment of samples and costly. In this paper, the optical characteristic of absorption of animal-based and plantbased oils are investigated to obtain fingerprints on UV-visible-near infrared wavelength region. Either animal-based or plant-based oils have absorption on wavelength region of 900 – 940 nm. However, each type of oils has unique absorption characteristics on wavelength region of 350 – 700 nm. Hence, the UV-visible region can be used as the marker of each type of edible oils.

The Mars 2020 “Perseverance” rover’s SuperCam instrument suite [1,2,3] provides a wide variety of active and passive remote sensing techniques [4, 5, 6, 7] including passive visible & near-infrared (“VISIR”) spectroscopy [8]. Here we present our plans to use the VISIR technique for atmospheric science by observing solar radiation scattered by the Martian sky, similar to the “passive sky” technique demonstrated with ChemCam on the Mars Science Laboratory (MSL) rover [9]. Our presentation will focus on the objectives and techniques of SuperCam VISIR atmospheric science, but we will also present initial atmospheric science results or relevant instrument performance validation results to the extent that such are available at the time of the conference.The objectives of VISIR atmospheric science are O2, CO, and H2O vapor column abundances, and aerosol particle sizes and composition. These objectives are motivated by unexpected seasonal and interannual variability in the O2mixing ratio that is argued to be so large as to require O2 sources and sinks in surface soils [10], by evidence of surface-atmosphere exchange of H2O [11], by the potential significance of O2 and H2O volatiles as field context for returned samples due to their active exchanges with surface materials, and by the Mars 2020 mission [12] objectives of characterizing dust and validating global atmospheric models to prepare for human explorationThe SuperCam spectrometers used for VISIR mode are a ChemCam-heritage reflection spectrometer covering 385–465 nm with < 0.2 nm res. [2], an intensified transmission spectrometer covering 536–853 nm with 0.3–0.7 nm res. [2], and an acousto-optic-tunable-filter (AOTF) -based IR spectrometer covering 1300–2600 nm with 20–30 cm-1 res. [1, 8]. Our primary observing strategy is the same approach used for MSL ChemCam “passive sky” observations [9]: ratioing instrument signals from the two pointing positions with different elevation angles eliminates solar spectrum and instrument response uncertainties that are ~100x and ~10x larger than signals of interest for the transmission and AOTF IR spectrometers, respectively. We will also make use of single pointings directed at the white SuperCam calibration target for less-resource-intensive water vapor and aerosol monitoring, and of multiple-pointing lower-signal-to-noise sky scans to better constrain aerosol size and shape. Sky radiance is fit with a discrete ordinates multiple scattering radiative transfer model identical to that of [9]. As in [9] gas abundances are made robust to aerosol scattering uncertainties by fitting CO2 absorption bands with an aerosol vertical profile parameter.References: [1] Maurice S. et al. (2020) SSR, in press. [2] Wiens R.C. et al. (2021) SSR 217, 4. [3] Manrique J.-A. et al. (2020) SSR 216, 138. [4] Ollila A.M. et al. (2021), this meeting. [5] Ollila A.M. et al. (2018) LPSC 49, 2786. [6] Forni O. et al. (2021), this meeting. [7] Lanza N. L. et al. (2021), this meeting. [8] Johnson J.R et al. (2021), this meeting. [9] McConnochie T.H et al. (2018), Icarus 307, 294. [10] Trainer M.G. et al. (2019), JGR 124, 3000. [11] Savijärvi H. et al. (2016), Icarus 265, 63. [12] Farley K.A. et al. (2020), SSR 216, 142.

The SuperCam Instrument Suite [1-4], a US-French-Spanish-Danish collaboration, consists of three separate units: the Body Unit (BU) within the Rover [2], the Mast Unit (MU) at the top of the Perseverance Remote Sensing Mast [3], and Calibration Targets [4] located on the rover deck. SuperCam includes a passive visible/near-infrared (VISIR) spectroscopy system that will identify minerals near the rover (mm-scale) to distant outcrops (m-scale) over an extended wavelength range (0.385-0.465 µm, 0.536-0.853 µm, 1.3-2.6 µm) that is diagnostic for most mineral classes.The infrared spectrometer (IRS) in the MU [5] uses an acousto-optic tunable filter (AOTF) excited by a RF signal to successively diffract up to 256 different wavelengths ranging between 1.3 and 2.6 µm on one of two available photodiodes to produce a single spectrum in about 80 seconds at a spectral resolution of 5-20 nm. The field-of-view (FOV) of the IRS is 1.15 mrad and is co-aligned with the RMI boresight. The visible (VIS) system in the BU comprises three spectrometers covering the UV (245 – 340 nm), violet (385 – 465 nm), and visible and near-infrared (VNIR, 536–853 nm). The spectrometers are fed by light collected by the telescope in the MU through an optical fiber connecting the MU and BU. The violet spectrometer has a spectral resolution of 0.12 nm, and the VNIR transmission spectrometer has a spectral resolution of 0.35 – 0.70 nm. The VIS FOV is 0.74 mrad and co-aligned with the IR FOV.Several SuperCam calibration targets (SCCT) are dedicated to VISIR spectroscopy, including an AluWhite white target, an Aeroglaze Z307 black target, and red, cyan, and green color targets [4]. Several of the other targets whose primary purpose is for other techniques exhibit useful VISIR spectral features and will be observed [5].Raw data will be converted to radiance (W/m2/sr/µm) with calibrated wavelengths using the instrument transfer function [6-7]. Relative reflectance spectra will be generated by dividing the calibrated radiance spectrum by either (1) a Mars atmospheric transmission spectrum and then by a modeled solar irradiance spectrum; or (2) a radiance spectrum of the white SCCT taken close in time to the surface observation, as is done with Mastcam-Z calibration [8].This poster will show initial VISIR data acquired on Mars, compared with test and performance data obtained at Paris Observatory, LANL, and JPL. As of this writing, the planned observations during the first ~30 sols include spectra of the white and black SCCTs, and at least one Mars target spectrum.[1] Farley et al. (2020), Space Sci. Rev. 216, 142. [2] Wiens et al. (2020) Space Sci. Rev. 216, in press, [3] Maurice et al. (2020) Space Sci. Rev. 216,in press, [4] Manrique et al. (2020) Space Sci. Rev. 216, 8, 1-27; [5] Cousin et al. (2021) this meeting [6] Fouchet et al. (2021) Icarus, in prep. [7] Royer et al. (2020) Rev. Scient. Instrum. 91, 063105. [8] Bell, J.F. et al. (2021), Space Sci. Rev, in press.

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.

Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R1830, R1834) and NDSI (R1830, R1834) with R2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD = 6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.

In critically ill patients, direct knowledge of intracellular pO2 would allow for identification of cellular hypoxia, which when prolonged leads to organ failure. We have developed a visible–near-infrared optical system that noninvasively measures myoglobin saturation, which is directly related to intracellular pO2, from the surface of the skin. We used an animal model of graded hypoxia from low levels of inspired oxygen ( n = 5) and verified that low intracellular pO2 is correlated with high steady-state serum lactate values. In addition, the pO2 gradient between arterial blood and inside muscle cells was 83 mm Hg at 21% O2, but fell to 24 mm Hg at 8% O2. Continuous myoglobin saturation measurement in skeletal muscle displayed the same trends as cerebral oxygenation with no lag in changes over time, demonstrating its relevance as a measure of systemic oxygenation.

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.

Skull optical clearing window permits us to perform in vivo cortical imaging without craniotomy, but mainly limits to visible (vis)-near infrared (NIR)-I light imaging. If the skull optical clearing window is available for NIR-II, the imaging depth will be further enhanced. Herein, we developed a vis-NIR-II skull optical clearing agents with deuterium oxide instead of water, which could make the skull transparent in the range of visible to NIR-II. Using a NIR-II excited third harmonic generation microscope, the cortical vasculature of mice could be clearly distinguished even at the depth of 650 μm through the vis-NIR-II skull clearing window. The imaging depth after clearing is close to that without skull, and increases by three times through turbid skull. Furthermore, the new skull optical clearing window promises to realize NIR-II laser-induced targeted injury of cortical single vessel. This work enhances the ability of NIR-II excited nonlinear imaging techniques for accessing to cortical neurovasculature in deep tissue.

Visible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R 2 C and R 2 CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R 2 C = 0.99, R 2 CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R 2 C = 0.93, R 2 CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

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.

This research was to study which orientation was better for freshness prediction of the white-shelled eggs using visible near infrared spectroscopy. The transmission spectra were acquired i...

Fine powders composed of tellurium grains of average size

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350–1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers.

Organic binder media in ochre rock art have so far resisted in-situ spectroscopic analysis. In this study visible near-infrared and short-wave infrared (VNIR-SWIR) spectroscopy was investigated for its usefulness in characterising experimental paints made with ochre and local (Weld Range, Western Australia) organic materials. VNIR-SWIR spectroscopy is a proven technique for the detection of mineral molecular compounds. Historic art conservators have successfully detected organic binder materials in 500-year old Renaissance art with near-infrared (NIR) spectroscopy. In this study, the chemical morphological spectral features of organic materials in newly made ochre paints, and UV accelerated 100-year aged ochre paints, were investigated. The analysis revealed complexities that would affect the detection of organic materials as anthropogenic in rock art. The first of these concerned the nature of the organic materials. There were significant deteriorative spectral changes caused by reactions initially with hematite (red ochre), and additional changes caused by accelerated UV light, resulting in remnant features different from the raw materials. These would render current references and databases useless for identification. The spectroscopic identification of organic binders in old rock art may depend on such remnants. Secondly, the composition of naturally occurring red ochre included organic materials as natural constituents, confounding the potential to differentiate those added by humans. Finally, this study has challenged the assumption that all organic materials, in particular animal fats, might function as binders to chemically adhere pigment. This paper presents a novel approach to the characterisation and identification of the constituents of ochre and organic paints, using VNIR-SWIR spectroscopy.

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.

The use of fast and non-destructive techniques for identifying sugarcane varieties enables the development of automatic sorting systems, contributing towards improving pre-processing steps in the alcohol and sugar industries. In this context, principal component analysis (PCA), factorial discriminant analysis (FDA), stepwise forward discriminant analysis (SFDA) and partial least-squares discriminant analysis (PLS-DA) were used to classify four Brazilian sugarcane varieties based on visible/near infrared (Vis/NIR) spectral reflectance measurements (450–1000 nm range) of stalks. All wavelengths contributed towards discriminating the sugarcane varieties, but the 600–750 nm range was most relevant. When evaluating PCA results considering the four sugarcane varieties, two of them overlapped and it was only possible to use classifiers of three varieties. Factorial discriminant analysis, PLS-DA and SFDA reached correct classifications of 0.81, 0.82 and 0.74, respectively, when considering the external validation data and the four sugarcane varieties evaluated. Results showed that Vis/NIR spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast sugarcane variety classification, which can be used in the agro-food industry or directly in the field.

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...

The aim of this work was to investigate the applicability of visible/near infrared (vis/NIR) spectroscopy comparing two immersion probes to evaluate the phytosanitary status of must at the grape receiving area to support wineries for an objective quantification of grape infection level. The experimentation was conducted on white and red varieties (Vitis vinifera L.), employing grapes naturally infected with the major wine grape diseases. A total of 159 musts were product and analysed by using a vis/NIR spectrophotometer coupled with two kind of immersion probes: a reflectance probe and a transmittance one. Classification analysis (Partial Least Squares – Discriminant Analysis, PLS-DA) was performed on musts spectra to test the performance of the spectrophotometer, combined with the two probes, to classify healthy and infected samples. Considering three different spectral range analysed (430–1000 nm; 1000–1650 nm; 430–1650 nm), the results obtained from PLS-DA models, in cross-validation, gave values of correctly classified samples (accuracy, %) between 52.5% and 90.4%, and ranged from 68.4% to 84.3% for reflectance and transmittance probes respectively. The optical system was tested in controlled laboratory conditions, simulating the desirable final use of device. A future real scale application could be envisaged inside the analysis tank at the grape receiving area, after setting the operative conditions to perform the measurements directly coupled with the traditional and common quality analysis (soluble solid content and acidity) performed on grape musts sampled at the grape consignment.