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Hemoglobin concentration is an indicator for assessing blood product quality. To measure hemoglobin concentration in blood products without damaging blood bags, we proposed a method based on visible-near infrared transmission spectroscopy. Complex optical properties of blood bag walls result in measurement irregularities. Analyses showed that the slope of the light intensity-pathlength curve was more robust to the influence of the blood bag wall. In this study, the transmission spectra of red blood cell suspensions at multiple optical pathlengths were obtained, and the slopes of logarithmic light intensity-pathlength curves were calculated through curve fitting. A nondestructive measurement of hemoglobin content was achieved by using a regression model correlating slope spectra and hemoglobin concentration. Sixty samples with hemoglobin concentrations ranging from 72 to 161 g/L were prepared. Among them, 40 samples were used as a calibration set, and the remaining 20 samples were used as a prediction set. The determination coefficient of the prediction set was 0.97, with a mean square error of 2.78 g/L. This result demonstrates that a non-destructive measurement of hemoglobin levels in blood bags can be achieved by multiple-pathlength transmission spectroscopy.

The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

The content of N, P and K in sugarcane leaves at seedling stage, tillering stage and elongation stage were detected rapidly by visible - near infrared spectrophotometer. A total of 123 leaf sample s' spectra were collected during the three growth periods. After the outliers were detected, 117 valid samples were obtained and spectral data of all samples were preprocessed. Using the spectral data processed by CARS-PCA as an independent variable, a 6-fold cross-validated PLS model for N, P, and K content was established. The R2 of the CARS-PCA-PLS model for N, P, K prediction were 0.8591, 0.6769, and 0.9321, respectively. Correlation analysis of the predicted N, P, and K contents were further implemented to explore the interaction effect between NPK. To simulate the interaction effect between those three major nutrients, 19 factors include possible linear, quadratic, cubic relations between NPK were assumed, multi-factor cubic polynomial regression PLS and MLR correction models were established from those factors. In the modified MLR model, the determinants of N, P and K were respectively 0.8908, 08019 and 0.9139, which improved the performance of the model by 3.7%, 18.5% and 1.3% compared with the CARS-PCA-PLS model which only based on the spectral reflectance data. The results show that the application of visible-near-infrared spectrum combined with interaction effects can effectively predict the content of N, P and K in the sugarcane growing stage. It provides important guiding significance for rapid real-time monitoring of sugarcane growth and fertility management.

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

In describing soil variability, information on the distribution of soil properties is required in both the horizontal and vertical directions. This study investigated the potential of a real-time soil sensor (RTSS) for mapping six soil properties at multiple soil depths of a paddy field. Soil spectra were acquired at three depths using RTSS. Three calibration models were developed. The first model (CM1) combined the dataset for depths of 10 and 15 cm, the second model (CM2) combined the dataset for depths of 15 and 20 cm, and the third model (CM3) combined all the three depths. CM3 was the best calibration model for all the soil properties. The generated maps exhibited variations in the distribution of all the soil properties at different depths.

This study aimed to evaluate the ability to use Vis-NIR spectroscopy to predict CaCO3 in the soil and to determine the contribution of the spectral ranges and wavelengths to the prediction. A total of 180 topsoil samples (0-25 cm) of anthropogenic soils derived from Flysch deposits in Dalmatia (Croatia) were analyzed for CaCO3 and scanned in the laboratory with an ASD FieldSpec spectroradiometer (350-2500 nm). The partial last square regression (PLSR) with leave-one-out cross-validation method was used for calibrating the Vis-NIR spectra and CaCO3 measured in the laboratory. The CaCO3 content in investigated soils varies within a very wide range from 186.0 to 894.7 g kg-1 and has a high an average value of 547.2 g kg-1 and normal - near symmetrical frequency distribution. Prediction parameters, the coefficient of determination (R2 ), the ratio of performance to deviation (RPD) and the range error ratio (RER) were 0.86, 2.42 and 11.4, respectively indicating that created PLSR model was able to predict CaCO3 content in soil with moderately successful accuracy. The prediction error of the CaCO3 measured as the root mean square error of prediction (RMSEP) was 57.9 g kg-1 . These results suggest that Vis-NIR spectroscopy in combination with PLSR is acceptable as a rapid method for quality control (screening) of the CaCO3 content in investigated soils.

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

The main objective of this study was to investigate the use of spectroscopic systems in the range of 400-1000nm (visible/near-infrared or Vis-NIR) and 900-1700nm (NIR) to assess and estimate plant and animal proteins as potential adulterants in minced beef and pork. Multiple machine learning techniques were used for classification, adulterant prediction, and wavelength selection. Samples were first evaluated for the presence or absence of adulterants (6 classes), and secondly for adulterant type (6 classes) and level. Selected wavelengths models generally resulted in better classification and prediction outputs than full wavelengths. The first stage classification rates were 96% and 100% for pure/unadulterated and adulterated samples, respectively. Whereas, the second stage had classification rates of 69-100%. The optimal models for predicting adulterant levels yielded correlation coefficient, r of 0.78-0.86 and ratio of performance to deviation, RPD, of 1.19-1.98. The results from this study illustrate potential application of spectroscopic technology to rapidly and accurately detect adulterants in minced beef and pork.

The improvement of living standards has urged consumers to pay more attention to the quality and nutrition of meat, so the development of nondestructive detection device for quality and nutritional parameters is commercioganic undoubtedly. In this research, a portable device equipped with visible (Vis) and near-infrared (NIR) spectrometers, tungsten halogen lamp, optical fiber, ring light guide and embedded computer was developed to realize simultaneous and fast detection of color (L*, a*, b*), pH, total volatile basic nitrogen (TVB-N), intramuscular fat (IF), protein and water content in pork. The wavelengths of dual-band spectrometers were 400~1100 nm and 940~1650 nm respectively and the tungsten halogen lamp cooperated with ring light guide to form a ring light source and provide appropriate illumination intensity for sample. Software was self-developed to control the functionality of dual-band spectrometers, set spectrometer parameters, acquire and process Vis/NIR spectroscopy and display the prediction results in real time. In order to obtain a robust and accurate prediction model, fresh longissimus dorsi meat was bought and placed in the refrigerator for 12 days to get pork samples with different freshness degrees. Besides, pork meat from three different parts including longissimus dorsi, haunch and lean meat was collected for the determination of IF, protein and water to make the reference values have a wider distribution range. After acquisition of Vis/NIR spectra, data from 400~1100 nm were pretreated with Savitzky-Golay (S-G) filter and standard normal variables transform (SNVT) and spectrum data from 940~1650 nm were preprocessed with SNVT. The anomalous were eliminated by Monte Carlo method based on model cluster analysis and then partial least square regression (PLSR) models based on single band (400~1100 nm or 940~1650 nm) and dual-band were established and compared. The results showed the optimal models for each parameter were built with correlation coefficients in prediction set of 0.9101, 0.9121, 0.8873, 0.9094, 0.9378, 0.9348, 0.9342 and 0.8882, respectively. It indicated this innovative and practical device can be a promising technology for nondestructive, fast and accurate detection of nutritional parameters in meat.

Apple trees (Malus domestica Borkh.) tend to exhibit a biennial cycle: a heavy-flowering year with an excessive amount of low-quality fruits is followed by a year with scarce flowering and low fruit load. Chemical thinning is currently the only viable solution in large commercial operations to ensure adequate yield. However, most thinners are effective only in the first few weeks following bloom and thinning efficiency depends on numerous factors and is difficult to predict. Forecasting the expected fruitlet drop after an initial thinner application would help perform corrections with the subsequent application. In this study, we used in-situ spectroscopy in the visible and near-infrared (Vis-NIR) range to forecast fruitlets drop rate. The study was carried out on “Golden Delicious” apple trees during two growing seasons – in April 2017 and April 2018. As commonly done in commercial orchards, the fruitlet drop was amplified by the application of synthetic auxins 1-naphthaleneacetic acid (NAA) and its amide (NAD). Fruitlets were tagged and monitored in situ every 2–4 days by measuring reflectance over the 400–1000 nm range. Special care was taken to assess sunlight interference during the measurement and correct it using a custom post-processing procedure. Measurements at 4–12 days after NAA/NAD treatment (days after treatment - DAT) were used to forecast fruitlet drop by 20–26 DAT (prediction dates). Principal component analysis (PCA(was carried out, followed by a classification algorithm (linear or quadratic discriminant analysis). Performing measurements on 4 DAT proved too early to predict fruitlet drop with satisfactory reliability (forecast accuracy 65%). Measurements at 6–12 DAT resulted in forecast accuracies of 80–97%, depending on the selected dates. The method offers a non-destructive prediction of apple fruitlet drop rate, which could lead to the development of a low-cost device that could help manage chemical thinning.

The present work aimed to carry out a preliminary study to verify the possibility of employing an optical, portable and inexpensive non-destructive device, based on vis/NIR, spectroscopy, directly on the production line of craft beer. Three types of craft beer were analyzed. For each type of craft beer, transflectance spectra were acquired in the wavelength range of 450–980 nm and at different stages of fermentation. Spectral sampling for each craft beer was conducted on filtered and non-filtered samples. The vis/NIR device was tested for the quick evaluation of soluble solid content (SSC) and pH. Spectra were elaborated in order to perform principal component analysis (PCA) and to build partial least square (PLS) regression models. The PCA results show that vis/NIR spectroscopy could be effective in discriminating between non-filtered (condition in the process line) and filtered samples. PLS models are promising for both the prediction of SSC and pH.

Chlorophyll fluorescence, VIS/NIR spectroscopy as well as biospeckle and backscattering at 635 nm, 690 nm, 830 nm, and 1060 nm, were used to detect changes in apples stored for 7 d (20 °C), either intact or subjected to high hydrostatic pressures: 100 MPa, 150 MPa and 200 MPa. Biospeckle at 635 nm, 690 nm and 830 nm, and backscattering at 690 nm, 830 nm and 1060 nm showed a high potential for determining changes during storage of intact fruit, while chlorophyll fluorescence, VIS/NIR spectroscopy and backscattering measured at 830 nm, and 1060 nm were appropriate to detect changes in fruit subjected to high hydrostatic pressures. Spectral measurements of apples treated with 150 and 200 MPa showed mainly textural changes and, during post-pressure storage, changes in chemical composition. Decrease in physiological activity and increased rates of enzymatic processes in pressure-treated apples may be responsible for the inability of biospeckle to detect pressure-induced changes.

Crucial physio-chemical changes occuring in eggs during storage after laying lead to loss of egg freshness. In this research, a new method for prediction of egg freshness using transmission visible near infrared spectroscopy was investigated. For this purpose 300 eggs were stored at two control conditions: refrigerator (4–5°C, 75%RH) and room (24–25°C, 40%RH) then by special egg holder, transmission spectroscopy was measured. For two eggs groups, 25 eggs in each group, in six days were tested by spectroscopy, after that Haugh unit and air cell height was measured directly. The non-destructive visible near infrared spectroscopy spectral measurements from 300 to 1100 nm (832 length of wave) were done as well as Haugh unit, air cell height for each egg and created the database for both environments. Finally a maximum likelihood latent root regression algorithm was developed to predict Haugh unit and air cell height by spectrum observation. The database was randomly divided into two parts. Training data, was ...

Black bean (Phaseolus vulgaris L.) processing pre- sents unique challenges because of discoloration, breakage, development of undesirable textures, and off-flavors during canning and storage. These quality issues strongly affect processing standards and consumer acceptance for beans. In this research, visible and near-infrared (Vis/NIR) reflectance data for the spectral region of 400-2,500 nm were acquired from intact dry beans for predicting five canning quality traits, i.e., hydration coefficient (HC), visual appearance (APP) and color (COL), washed drained coefficient (WDC), and texture (TXT), using partial least squares regression (PLSR). A total of 471 bean samples harvested and canned in 2010, 2011, and 2012 were used for analysis. PLSR models based on the Vis/ NIR data showed low predictive performance, as measured by correlation coefficient for prediction (Rpred )f or APP (Rpred= 0.275-0.566) and TXT (Rpred=0.270-0.681), but better results for predicting HC (Rpred=0.517-0.810), WDC (Rpred=0.420- 0.796), and COL (Rpred

With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R2V = 0.73, RMSEV = 2.78 g∙kg−1 and RPDV = 1.89) and outperformed the SVMR model (R2V = 0.72, RMSEV = 2.83 g∙kg−1 and RPDV = 1.86) and the SPA-PLSR model (R2V = 0.62, RMSEV = 3.23 g∙kg−1 and RPDV = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

Soil spectroscopy in the visible-to-near-infrared (vis-NIR) range is a cost-effective alternative analysis technique to determine soil organic carbon (SOC). The development and provision of large-scale soil spectral libraries (SSLs) further facilitates the application of vis-NIR spectroscopy for the rapid assessment of SOC. However, optimal strategies to apply SOC calibrations from SSLs to independent field sites have yet to be established. We tested the predictive ability of SOC calibrations based on three external SSLs at the national, regional and field scale by applying them to two field sites in Germany. The national-scale SSL was comprised as a subset of the European LUCAS 2009 topsoil database. This subset was further classified into a randomly selected subset and target site-specific subsets based on similarity of spectral characteristics and soil parent material. A regional- and a field-scale legacy dataset were additionally used to predict SOC at the two field sites and to compare the results with the performance of the LUCAS based models. SSL-based predictive models adapted to the characteristics of the target sites by means of spiking were evaluated against purely local calibrations. Models calibrated with spectra from the LUCAS library and the regional-scale dataset predicted the SOC contents of the target field sites generally poorly (0.45 ≤ RPD ≤ 2.08), largely as a result of biased estimates. Spiking the models with only a few (~15) samples from the target sites reduced prediction bias drastically and thus yielded markedly improved SOC estimates for nearly all redeveloped models (1.30 ≤ RPD ≤ 3.69). Spiking the models based on the field-scale SSL with 15 samples produced better results than the spiked larger national and regional calibration sets, with RPD values of 5.66 and 4.14 for both target sites. Our results suggest that universal calibrations based exclusively on library spectra of larger scale are insufficient for accurate SOC assessments at the local scale even with pedogenetically or spectrally adapted calibration subsets. Spiking the vis-NIR models based on SSLs with a small number of target site samples allows a successful transfer of SOC calibrations, but does not necessarily yield more accurate predictions than local models developed exclusively with the spiking samples or calibrations based on field-scale SSL with similar characteristics, which may be preferable for model development if available.

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to , and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Developing mechanical harvesting for table olives will require decreasing fruit damage during harvest and postharvest handling, transport and storage. The susceptibility to bruising and its development over time were studied in three table olive varieties, cv. ‘Manzanilla’, ‘Gordal Sevillana’ and ‘Hojiblanca’. Bruising was produced with controlled energy impacts of 56, 26, 13 mJ. A strong correlation ( r 2 = 0.77–0.90) between bruise volume and impact energy was demonstrated. Bruise susceptibility was higher in the Manzanilla variety, followed by Hojiblanca and Gordal Sevillana cultivars. Bruise time evolution was evaluated using a spectrophotometer for visible and near infrared regions. A bruise index was developed using different wavelengths, 545, 670 and 800 nm. Most darkening due to the browning process happened within 1 h, was exponential and dependent on impact energy level. The discoloration was greatest in the Manzanilla, followed by Hojiblanca and Gordal Sevillana olives.

As one of the most widely consumed alcoholic beverages, Chinese liquor varies greatly in price, flavor, and quality. This diversity calls for effective and reliable discrimination methods. In an attempt to find the best liquor discrimination method, this study used different methods to analyze and identify 730 Chinese liquor samples including 22 kinds, ten brands, and six flavors. These samples, covering most of the famous liquors in China, were analyzed by visible and near-infrared (Vis/NIR) spectroscopy and modeled by three classification methods including supporting vector machine, soft independent modeling of class analogy, and linear discriminate analysis based on principal component analysis (PCA-LDA). Pretreatments and parameters for each model were optimized, and models discrimination ability was compared. The research finds that PCA-LDA was the best model with an average prediction rate of 98.94 % in the training set and 95.70 % in the test set. The correct rates for brands, flavor styles, ages, and alcohol degrees were all higher than 95 %. It shows that Vis/NIR is a reliable, inexpensive, and effective tool for Chinese liquors discrimination.

A prototype bulk density sensor (PBDS) to assess soil bulk density (BD) has been developed and tested for top soil (0–15 cm). It is a multi-sensor kit, consisting of a penetrometer equipped with a visible and near-infrared (vis-NIR) spectrophotometer. Artificial neural network (ANN) was used to develop a BD prediction model, as a function of penetration resistance (PR), soil moisture content (MC), organic matter content (OMC) and clay content (CLC), using 471 samples collected from various fields across four European countries, namely, Czech Republic, Denmark, the Netherlands and the UK. While penetration resistance (PR) was measured with a standard penetrometer (30 degree cone of 1.26 cm2 cone-base area), MC, OMC and CLC were predicted with a vis-NIR (1650–2500 nm) spectrophotometer (Avantes, Eerbeek, The Netherlands). ANN was also used to model the vis-NIR spectra to predict MC, OMC and CLC. The PBDS was validated by predicting topsoil (0–0.15 m) BD of three selected validation fields in Silsoe experimental farm, the UK. The ANN BD model performed very well in training (coefficient of determination (R2) = 0.92 and root mean square error (RMSE) = 0.05 Mg m−3), validation (R2 = 0.84 and RMSE = 0.08 Mg m−3) and testing (R2 = 0.94 and RMSE = 0.04 Mg m−3). The validation of PBDS for BD assessment in the three validation fields provided high prediction accuracy, with the highest accuracy obtained in Downing field (R2 = 0.95 and RMSE = 0.02 Mg m−3). It can be concluded that the new prototype sensor to predict BD based on, a standard penetrometer equipped with a vis-NIR spectrophotometer and ANN model can be used for in situ assessment of BD. The PBDS can also be recommended to provide information about soil MC, OMC and CLC, as the ANN vis-NIR calibration models of these properties were of excellent performance.

We investigated the feasibility of visible–near-infrared (Vis–NIR) spectroscopy for estimation of wood qualities of fast-growing Eucalyptus grandis. Partial least squares regression (PLSR) models are applied to predict the diameter at the breast height (DBH), lateral growth rate (LGR) and propagation velocity of stress waves (PVSW). It was possible to estimate LGR and PVSW with appropriate accuracy. This suggested that perhaps information in terms of maturation is included in Vis–NIR spectra. The key factors in the validation of PVSW and LGR were the water and cellulose condition in wood.

Berry fruit farming and marketing have widely increased in recent years in marginal areas of Italy, especially in response to consumers’ growing interest in adhering to a bioactive and health-protecting diet. The ripening stage of berry fruits, on which their quality and nutraceutical attributes depend, is typically estimated by growers through visual inspection based on the growers’ experience. Low-cost, handheld, user-friendly devices could assist growers in the field in determining the optimal harvest date in accordance with the desired ripeness of berries. In order to explore the technical feasibility of such a system, this study focused on defining a simple ripeness index for blueberries by identifying the main spectral changes accompanying their ripening process, with special attention paid to the last and most relevant stages. With this aim, spectral measurements in the range of 445 to 970 nm were carried out on Vaccinium corymbosum (cv. Brigitta) during two different growing seasons. Spectra were acquired on 942 individual berries in the field at different dates. Measurements were accompanied by weekly samplings, with picked fruits divided into four ripeness grades according to commercial growers’ classification. A principal component analysis of fruit spectra highlighted that two main wavebands (680 and 740 nm) can maximize the differences between fully ripe samples and those close to ripeness or unripe. Hence, spectral values at 680, 740, and 850 nm (the latter being an additional normalization waveband) were used to create a blueberry ripeness index (BRI) as a linear combination of two spectral ratios: S1 = log(I680/I850) and S2 = (I740/I850). The definition of specific ripeness thresholds for the BRI according to more or less selective criteria was illustrated in an application example, and the ripeness classification capability was then assessed on a separate validation set of 471 berries. When applying a less selective threshold approach, the BRI correctly classified as ripe 85% of manually graded fully ripe berries, whereas 13% of close but not yet ripe validation samples were misclassified as fully ripe and ready to harvest. Comparatively, when a more demanding ripeness threshold was applied, the amount of nearly ripe berries misclassified as ripe decreased to 8%, but the amount of fully ripe berries not identified as ripe rose to 25%. In both cases, none of the unripe validation samples was erroneously classified as ripe fruit. These results, which were obtained with a BRI defined by spectral measurements at just three discrete wavelengths, point to the feasibility of a simple, microcontroller-based, handheld optical device able to implement the BRI to quickly assess the ripeness of sets of berries during the last and most delicate stages of the ripening process.

The feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels ( Corylus avellana L. cv. Tonda Gentile Romana) is demonstrated. Feature datasets comprising raw absorbance values, raw absorbance ratios ( Abs [ λ 1 ]: Abs [ λ 2 ]) and differences ( Abs [ λ 1 ] − Abs [ λ 2 ]) for all possible pairs of wavelengths from 306.5 nm to 1710.9 nm were extracted from the spectra for use in an iterative LDA routine. For each dataset, several spectral pretreatments were tested. Each group of features selected was subjected to Partial Least Squares Discriminant Analysis (PLS-DA), Receiver Operating Characteristics (ROCs) analysis, and evaluation of performance through the Area Under ROC Curve. The best result (5.4% false negative, 5.0% false positive, 5.2% total error) was obtained using a Savitzky–Golay second derivative on the dataset of raw absorbance differences. The optimal features were Abs [564 nm]– Abs [600 nm], Abs [1223 nm]– Abs [1338 nm] and Abs [1283 nm]– Abs [1338 nm]. The results indicate the feasibility of a rapid, online detection system.

Purpose: The present study focused on the estimation of soluble solids content (SSC) of chestnut using reflectance and transmittance spectra in range of VIS/NIR. Methods: Four species intact/peeled chestnuts were used for acquisition of spectral data. Transmittance and reflectance spectra were used to develop the best PLS model to estimate SSC of chestnut. Results: The model developed with the transmitted energy spectra of peeled chestnuts rather than intact chestnuts and with range of NIR rather than VIS performed better. The best R 2 and RMSEP of cross validation were represented as 0.54 and 1.85 ˚Brix. The results presented that the reflectance spectra of peeled chestnuts by species showed the best performance to predict SSC of chestnut. R 2 and RMSEP were 0.55 and 1.67 ˚Brix. Conclusions: All developed models showed RMSEP around 1.44 ~ 2.54 ˚Brix, which is considered not enough to estimate SSC accurately. It was noted that R 2 of cross validation that we found were not high. For all that, grading of the fruits in two or three classes of SSC during postharvest handling seems possible with an inexpensive spectrophotometer. Furthermore, th e development of estimation of SSC by each chestnut species could be considered in that SSC distribution is clustering in different range by species.

Conventional analyses of soil characteristic are expensive and time-consuming. Visible and near infrared reflectance spectroscopy (VIS–NIR) have been useful tools for quantitative analysis of numerous soil attributes. In this study, the fidelity of spatial structure of soil attributes was evaluated by geostatistical methods and elemental concentrations were mapped using Hyperion hyperspectral reflectance data (400–2500 nm). Forty-nine soil samples were used to analyze soil organic carbon (SOC), total phosphorus (TP), pH, and cation exchange capacity (CEC). The performance of three different instrumental settings (laboratory, Hyperion and simulated Hyperion spectroscopy) was assessed using either partial least squares regression (PLSR) or stepwise multiple linear regression (SMLR). Models for SOC, TP, and pH showed moderate accuracy (R2 > 0.6, RPD > 1.5), whereas that for CEC exhibited low efficiency (R2

Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate change and agricultural practices is challenging because existing SOC monitoring methods are very time and resource consuming. This study examined the use of visible near-infrared spectroscopy (Vis-NIR) as a speedy method to predict SOC and to monitor spatial and temporal changes in SOC compared with labor-intensive traditional laboratory (TL) measurements. For SOC prediction, topsoil (0-25 cm) and subsoil (25-50 cm) samples in the Danish soil spectral library for the years 1986 and 2009 were used. Empirical Bayesian Kriging was used to map SOC. The Vis-NIR predictions indicated that average topsoil and subsoil SOC had decreased slightly in Denmark from 1986 to 2009, and this was confirmed by TL measurements of SOC. In East Denmark, Vis-NIR predictions differed significantly from the measured SOC values. For subsoil samples, the ability of Vis-NIR to predict SOC levels varied. In West Jutland, Central Jutland, North Jutland, and Thy, Vis-NIR-predicted SOC levels did not differ from TL-measured levels, showing good predictive ability. For topsoil samples, the spatial pattern of change in TL-measured and predicted SOC was consistent during the 23-year study period, but there were significant discrepancies in the corresponding SOC change patterns for subsoil samples. To conclude, Vis-NIR is a promising method for monitoring spatial and temporal changes in SOC at the national scale, especially in the topsoil. Some difficulties can arise in low SOC subsoils, so more systematic work is needed to improve the method for practical applications.

Internal defects of potatoes are difficult to detect. Defect potatoes need to be removed as they cause complaints from customers and may spoil a whole patch in later food processing stages. We evaluated light transmittance for the sensing of internal defects in their different stages. The results are very promising: the classification rate for healthy and defect potatoes with skin is 90 % and without skin 92.5 %.

Visible/near-infrared spectroscopy has been evaluated for use in freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets, where fresh samples were stored as whole fish in ice. A handheld interactance probe for performing rapid measurements of single fillets and an imaging spectrometer for online analysis at an industrial speed of one fillet per second, have been used. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozen-thawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previous results using the Quality Index Method with trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is appropriate for both frozen-thawed classification and freshness prediction of salmon fillets. The results indicate that the spectral changes are explained mainly by oxidation of heme proteins during the freeze–thaw cycle and during chilled storage in ice.

The potential of VIS-NIR spectroscopy as a rapid screening method for resistance of Fusarium-inoculated oats to replace the costly chemical measurements of deoxynivalenol (DON) was investigated. Partial least squares (PLS) regression was conducted on second-derivative spectra (400–2,350 nm) of 166 DON-contaminated samples (0.05–28.1 ppm, mean = 13.06 ppm) with separate calibration and test set samples. The calibration set had 111 samples, and the test set had 55 samples. The best model developed had three PLS components and a root mean square error of prediction (RMSEP) of 3.16 ppm. The residual predictive deviation (RPD) value of the prediction model was 2.63, an acceptable value for the purpose of rough screening. Visual inspection and the VIS spectra of the samples revealed that high-DON samples tended to be darker in color and coarser in texture compared with low-DON samples. The second-derivative spectra showed that low-DON samples tended to have more water and fat content than high-DON samples. With an RMSEP value of 3.16 and RPD of value of 2.63, it seems possible to use VIS-NIR spectroscopy to semiquantitatively estimate DON content of oats and discard the worst genotypes during the early stages of screening.

Four kinds of blueberry beverage from different varieties, a total of 140 samples were acquired and analyzed by applying of spectrum technology. Using Savitzky-Golay spectral smoothing and multiplicative scatter correction (MSC) on the sample data pretreatment, four varieties of blueberry beverage were cluster analyzed by using principal component analysis method (PCA),a three-dimensional score view was achieved by the first 3 principal components of all samples (PC1, PC2 and PC3), which shows an obvious classification effect on the blueberry beverage. The first three principal components of the load diagram analysis, the characteristic bands related with the blueberry beverage varieties were 420-430nm, 490-500nm, 570-580nm and 1350-1365nm. According to the cumulative contribution rate (99.20%) of the first 6 principal components, the first 6 principal components was choosed as the input of multilayer perceptron (MLP) neural network, 100 samples in all the blueberry beverage samples were selected as a training set, and the remaining 40 samples were used as the prediction set. Training set were trained and prediction set were predicted by applying the multilayer perceptron neural network, and the correct rate of prediction were 100%.Research shows, using principal component analysis combined with multilayer perceptron neural network to identify the varieties of blueberry beverage is feasible.

A rapid analytical method of human whole blood viscosity with low, medium, and high shear rates [WBV(L), WBV(M), and WBV(H), respectively] was developed using visible and near-infrared (Vis-NIR) spectroscopy combined with a moving-window partial least squares (MW-PLS) method. Two groups of peripheral blood samples were collected for modeling and validation. Separate analytical models were established for male and female groups to avoid interference in different gender groups and improve the homogeneity and prediction accuracy. Modeling was performed for multiple divisions of calibration and prediction sets to avoid over-fitting and achieve parameter stability. The joint analysis models for three indicators were selected through comprehensive evaluation of MW-PLS. The selected joint analysis models were 812–1278 nm for males and 670–1146 nm for females. The root-mean-square errors (SEP) and the correlation coefficients of prediction (RP) for all validation samples were 0.54 mPa•s and 0.91 for WBV(L), 0.25 mPa•s and 0.92 for WBV(M), and 0.22 mPa•s and 0.90 for WBV(H). Results indicated high prediction accuracy, with prediction values similar to the clinically measured values. Overall, the findings confirmed the feasibility of whole blood viscosity quantification based on Vis-NIR spectroscopy with MW-PLS. The proposed rapid and simple technique is a promising tool for surveillance, control, and treatment of cardio-cerebrovascular diseases in large populations.

The major issues concerning to food products are related to its authenticity. Honey is one of the common food products that suffer from adulteration, mainly due to its constant high market demand and price. Several studies on the authenticity detection have been done mainly on honey from genus Apis (GA), but less research has been conducted on Stingless Bee Honey (SBH) even the market demand for this food product is increasing, particularly in Malaysia due to its possible health benefits. Thus, identification of unadulterated and authenticity of honey is a very key issue for products processors, retailers, consumers and regulatory authorities. There is an increasing demand for appropriate instruments and methods to shield consumers against fraud and to guarantee a fair competition between honey producers. The study presented in this paper shows the effect of diluting pure honey from both genus Apis and Stingless Bee towards its physicochemical attributes (i.e. soluble solids content and pH) and VIS-NIR spectral absorbance features.

Background Influenza patients show a severe condition of the respiratory tract with high temperature. Efficient treatment of influenza requires early use of oseltamivir, and thus rapid diagnosis is needed. Recently, rapid diagnostic methods such as immunochromatography have been developed; however, immunochromatography is not an optimal technique because it is relatively expensive and has low sensitivity. Methods Visible and near-infrared (Vis-NIR) spectroscopy in the region 600–1100 nm, combined with chemometrics analysis such as principal component analysis (PCA) or soft modeling of class analogy (SIMCA), was used to develop a potential diagnostic method for influenza based on nasal aspirates from infected patients. Results The Vis-NIR spectra of nasal aspirates from 33 non-influenza patients and 34 influenza patients were subjected to PCA and SIMCA to develop multivariate models to discriminate between influenza and non-influenza patients. These models were further assessed by the prediction of 126 masked measurements [30 from non-influenza patients, 30 from influenza patients and 66 from patients infected with respiratory syncytial virus (RSV)]. The PCA model showed some discrimination of the masked samples. The SIMCA model correctly predicted 29 of 30 (96.7%) non-influenza patients, and 30 of 30 (100%) influenza patients from the Vis-NIR spectra of masked nasal aspirate samples. Nasal aspirates of RSV-infected patients were predicted as 50% non-influenza and 50% influenza by the SIMCA model, suggesting that discrimination between patients infected with influenza virus and those infected with RSV was difficult. Conclusions Although the study sample was small and there was difficulty in discriminating between influenza virus and RSV infection, these results suggest that Vis-NIR spectroscopy of nasal aspirates, combined with chemometrics analysis, might be a potential tool for diagnosis of influenza.

Bruise damage on table olives is the main factor that reduces fruit quality and leads to a severe loss of product during harvesting and postharvest handling operations. The Manzanilla cultivar is the most important table olive variety and it is also very susceptible to the formation of bruises. In this study, visible and near-infrared spectroscopy techniques were used to detect bruise damage on fruit with different absorbed impact energy levels (low = 13 ± 2 mJ, medium = 27 ± 2 mJ and high = 58 ± 7 mJ). The visible spectral region of 535–680 nm was used to distinguish between undamaged and damaged fruit for qualitative analysis. The greater differences in reflectance in the near-infrared region of 700–950 nm enabled a good quantitative analysis by distinguishing between the different impact energy levels. Modified partial least square models were developed to determine the bruise volume in damaged fruits and the absorbed energy during impact. Good fits (r2 = 0.87–0.90) were obtained between the values predicted by visible and near infrared spectroscopy and the values measured in laboratory by the reference methods for bruise volume and absorbed impact energy.

This article proposes a new method for fast discrimination of irradiation doses of sucrose based on visible-near infrared (Vis/NIR) spectroscopy technology. 250 sucrose samples were categorized into five groups to be irradiated at 0, 1.5, 3.0, 4.5, 6.0 kGy respectively and prepared for the discrimination analysis. The 50 samples of each group were randomly divided into a calibration set containing 40 samples, and a validation set containing the remaining 10 samples. Principal component clustering analysis (PCCA) was applied for the extraction of principal components (PCs) and for clustering analysis. The first five PCs were regarded as the inputs to develop the back propagation neural network (BPNN) model. The performance of the model was validated by the 50 unknown samples and the BPNN achieved an excellent precision and recognition ration of 100%. The results indicated that Vis/NIR spectroscopy could be utilized as a rapid and non-destructive method for the classification of different irradiation doses of irradiated sucrose.

This paper reports a novel application of hyperspectral imaging (a spectroscopic technique) for measuring wood density profiles at the growth ring scale. The measurements were performed with a spatial resolution of 79 µm. In the present case, hyperspectral imaging was used to measure wood sample reflectance for light in the wavelength range between 380 and 1028 nm, with a resolution of approximately 0.6 nm. The work was performed with 34 samples collected from 34 trees of Pinus pinea. A total of 34,093 density points were used to create and validate a partial least-squares (PLS) regression that converts spectroscopic reflectance data into density values. The coefficient of determination value between the present method and X-ray microdensitometry is 0.810 with a root mean squared error of 6.54×10-2 g.cm-3.

Recently, non-destructive techniques such as the Vis / NIR spectroscopy have been used to evaluate the characteristics of maturation and quality of pears. The study aims to validate the readings by the Vis / NIR spectroscopy as a non-destructive way to assess the qualitative characteristics of pear cultivars 'Williams', 'Packams' and 'Carrick', produced according to Brazilian conditions. The experiment was conducted at the Pelotas Federal University, UFPel, in Pelotas / RS, and the instrument used to measure the fruit quality in a non-destructive way was the NIR- Case spectrophotometer (SACMI, Imola, Italy). To determine pears' soluble solids (SS) and pulp firmness (PF), it was established calibration equations for each variety studied, done from the evaluations obtained by a non-destructive method (NIR-Case) and a destructive method. Further on, it was tested the performance of these readings by linear regressions. The results were significant for the soluble solids parameter obtained by the Vis / NIR spectroscopy; however, it did not achieve satisfactory results for the pear pulp firmness of these cultivars. It is concluded that the Vis / NIR spectroscopy, using linear regression, allows providing reliable estimates of pears' quality levels, especially for soluble solids.Recentemente, técnicas não destrutivas como a espectroscopia Vis/NIR têm sido utilizadas para avaliar as características de maturação e qualidade das peras. O trabalho tem como objetivo validar as leituras por espectroscopia Vis/NIR, como forma não destrutiva de avaliar as características qualitativas em peras das cultivares Williams, Packams e Carrick produzidas em condições brasileiras. O experimento foi realizado na Universidade Federal de Pelotas, UFPel, Pelotas/RS e o instrumento utilizado para determinar a qualidade dos frutos de forma não destrutiva foi o espectrofotômetro NIR-Case (SACMI, Imola, Itália). Para a determinação de sólidos solúveis (SS) e firmeza da polpa (FP) das peras, estabeleceu se a calibração para cada cultivar em estudo a partir das avaliações obtidas pelo método não destrutivo (NIR-Case) e pelo método destrutivo e, posteriormente, testou-se o desempenho destas leituras através de regressões lineares. Os resultados foram significativos para o parâmetro sólidos solúveis obtido por espectroscopia Vis/NIR, no entanto, não se obteve resultados satisfatórios para a firmeza da polpa de peras dessas cultivares. Conclui-se que a espectroscopia Vis/NIR utilizando regressão linear possibilita fornecer estimativas seguras dos índices de qualidade para peras, especialmente para os sólidos solúveis.

The feasibility of reflectance Vis/NIR spectroscopy was investigated for taste characterization of Valencia oranges based on taste attributes including soluble solids content (SSC) and titratable acidity (TA), as well as taste indices including SSC to TA ratio (SSC/TA) and BrimA. The robustness of multivariate analysis in terms of prediction was also assessed. Several combinations of various preprocessing techniques with moving average and Savitzky-Golay smoothing filters, standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before calibration and the models were developed based on both partial least squares (PLS) and principle component regression (PCR) methods. The best models obtained with PLS method had root mean square errors of prediction (RMSEP) of 0.33^oBrix, 0.07%, 1.03 and 0.37, and prediction correlation coefficients (r"p) of 0.96, 0.86, 0.87 and 0.92 for SSC, TA, SSC/TA, and BrimA, respectively. It was concluded that Vis/NIR spectroscopy combined with chemometrics could be an accurate and fast method for nondestructive prediction of taste attributes and indices of Valencia oranges. Moreover, the application of this technique was suggested for taste characterization, directly based on BrimA which is the best index related to fruit flavor rather than determination of SSC or TA alone.