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Abdul Mounem Mouazen - One of the best experts on this subject based on the ideXlab platform.
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on line vis nir Spectroscopy prediction of soil organic carbon using machine learning
Soil & Tillage Research, 2019Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Accurate on-line visible and near infrared (Vis-NIR) Spectroscopy prediction of soil organic carbon (OC) is essential for food security and environmental management. This paper aims at using on-line Vis-NIR spectra coupled with random forest (RF) modelling approach for the prediction of soil organic carbon (OC), comparing between single field (SF), non-spiked UK multiple-field (NSUK) and spiked UK multiple-field (SUK) calibration models. Fresh soil samples collected from 6 fields in the UK (including two target fields) were scanned with a fibre-type Vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration and independent validation sets, RF was run on the calibration set to develop calibration models for OC for the three studied datasets. Results showed that the model prediction performance depends on the dataset used and varies between fields. Less accurate prediction performance was obtained for the on-line prediction compared to laboratory (samples scanned in the laboratory under non-mobile measurement) prediction, and for non-spiked models compared to spiked models. The best model performance in both laboratory and on-line predictions was obtained when samples from the SF were spiked into the UK samples, with coefficients of determination (R2) values of 0.80 to 0.84 and 0.74 to 0.75, root mean square error of prediction (RMSEP) values of 0.14% and 0.17 to 0.18%, and ratio of prediction deviation (RPD) values of 2.30 to 2.5 and 1.98 to 2.04, respectively. Therefore, these results suggest that RF modelling approach when coupled with spiking provides high prediction performance of OC under both non-mobile laboratory and on-line field scanning conditions.
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optimal sample selection for measurement of soil organic carbon using on line vis nir Spectroscopy
Computers and Electronics in Agriculture, 2018Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Abstract The selection of samples for modelling of visible and near infrared (Vis-NIR) spectra for prediction of soil organic carbon (OC) is a crucial step for improving model prediction performance. This paper aims at comparing three soil sample selection methods coupled with spiking technique for improving on-line prediction performance of OC. Sample selection methods included random selection (RS), Kennard-Stone (KS) algorithm and similarity analysis (SA). Soil Vis-NIR spectra was measured with an on-line fibre-type Vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. A multiple field sample set (268 samples) was merged with samples (148 samples) collected from one target field, and the resulted sample set was subjected to the three sample selection methods. After dividing spectra into calibration and prediction sets, partial least squares regression (PLSR) was run on the calibration set to develop calibration models for OC, and resulted models were validated using samples of the prediction set. Results show that SA performed generally better than its competitors, especially when there were 58 spiked samples used in the calibration set (54% of total spiked samples of 106), with the best residual prediction deviation (RPD) and root mean squares error of prediction (RMSEP) of 2.14–2.54 and 0.16–0.15% for laboratory and on-line prediction. KS and RS performed similarly, but depending on the size of the calibration set, KS produced slightly better models. This indicates that the proposed SA coupled with spiking holds great potential in the optimization of a calibration set size and may serve as a novel and efficient tool for balancing the cost and quality of vis–NIR calibrations for estimating OC.
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comparison between random forests artificial neural networks and gradient boosted machines methods of on line vis nir Spectroscopy measurements of soil total nitrogen and total carbon
Sensors, 2017Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (Vis-NIR) Spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, Vis-NIR spectrophotometer (305-2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R²) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R² and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions.
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machine learning based prediction of soil total nitrogen organic carbon and moisture content by using vis nir Spectroscopy
Biosystems Engineering, 2016Co-Authors: Antonios Morellos, Xanthoula Eirini Pantazi, Dimitrios Moshou, Thomas Alexandridis, Rebecca L Whetton, Georgios Tziotzios, Jens Wiebensohn, Ralf Bill, Abdul Mounem MouazenAbstract:It is widely known that the visible and near infrared (Vis-NIR) Spectroscopy has the potential of estimating soil total nitrogen (TN), organic carbon (OC) and moisture content (MC) due to the direct spectral responses these properties have in the near infrared (NIR) region. However, improving the prediction accuracy requires advanced modelling techniques, particularly when measurement is planned for fresh (wet and un-processed) soil samples. The aim of this work is to compare the predictive performance of two linear multivariate and two machine learning methods for TN, OC and MC. The two multivariate methods investigated included principal component regression (PCR) and partial least squares regression (PLSR), whereas the machine learning methods included least squares support vector machines (LS-SVM), and Cubist. A mobile, fibre type, Vis-NIR spectrophotometer was utilised to collect soil spectra (305–2200 nm) in diffuse reflectance mode from 140 wet soil samples collected from one field in Germany. The results indicate that machine learning methods are capable of tackling non-linear problems in the dataset. LS-SVMs and the Cubist method out-performed the linear multivariate methods for the prediction of all three soil properties studied. LS-SVM provided the best prediction for MC (root mean square error of prediction (RMSEP) = 0.457% and residual prediction deviation (RPD) = 2.24) and OC (RMSEP = 0.062% and RPD = 2.20), whereas the Cubist method provided the best prediction for TN (RMSEP = 0.071 and RPD = 1.96).
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non biased prediction of soil organic carbon and total nitrogen with vis nir Spectroscopy as affected by soil moisture content and texture
Biosystems Engineering, 2013Co-Authors: Boyan Kuang, Abdul Mounem MouazenAbstract:This study was undertaken to evaluate the effects of moisture content (MC) and texture on the prediction of soil organic carbon (OC) and total nitrogen (TN) with visible and near infrared (vis–NIR) Spectroscopy under laboratory and on-line measurement conditions. An AgroSpec spectrophotometer was used to develop calibration models of OC and TN using laboratory scanned spectra of fresh and processed soil samples collected from five fields on Silsoe Farm, UK. A previously developed on-line vis–NIR sensor was used to scan these fields. Based on residual prediction deviation (RPD), which is the standard deviation of the prediction set (S.D.) divided by the root mean square error of prediction (RMSEP), the validation of partial least squares (PLS) models of OC and TN prediction using on-line spectra was evaluated as very good (RPD = 2.01–2.24) and good to excellent (RPD = 1.86–2.58), respectively. A better accuracy was obtained with fresh soil samples for OC (RPD = 2.11–2.34) and TN (RPD = 1.91–2.64), whereas the best accuracy for OC (RPD = 2.66–3.39) and TN (RPD = 2.85–3.45) was obtained for processed soil samples. Results also showed that MC is the main factor that decreases measurement accuracy of both on-line and fresh samples, whilst the accuracy was greatest for soils of high clay content. It is recommended that measurements of TN and OC under on-line and laboratory fresh soil conditions are made when soils are dry, particularly in fields with high clay content.
Said Nawar - One of the best experts on this subject based on the ideXlab platform.
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on line vis nir Spectroscopy prediction of soil organic carbon using machine learning
Soil & Tillage Research, 2019Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Accurate on-line visible and near infrared (Vis-NIR) Spectroscopy prediction of soil organic carbon (OC) is essential for food security and environmental management. This paper aims at using on-line Vis-NIR spectra coupled with random forest (RF) modelling approach for the prediction of soil organic carbon (OC), comparing between single field (SF), non-spiked UK multiple-field (NSUK) and spiked UK multiple-field (SUK) calibration models. Fresh soil samples collected from 6 fields in the UK (including two target fields) were scanned with a fibre-type Vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration and independent validation sets, RF was run on the calibration set to develop calibration models for OC for the three studied datasets. Results showed that the model prediction performance depends on the dataset used and varies between fields. Less accurate prediction performance was obtained for the on-line prediction compared to laboratory (samples scanned in the laboratory under non-mobile measurement) prediction, and for non-spiked models compared to spiked models. The best model performance in both laboratory and on-line predictions was obtained when samples from the SF were spiked into the UK samples, with coefficients of determination (R2) values of 0.80 to 0.84 and 0.74 to 0.75, root mean square error of prediction (RMSEP) values of 0.14% and 0.17 to 0.18%, and ratio of prediction deviation (RPD) values of 2.30 to 2.5 and 1.98 to 2.04, respectively. Therefore, these results suggest that RF modelling approach when coupled with spiking provides high prediction performance of OC under both non-mobile laboratory and on-line field scanning conditions.
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optimal sample selection for measurement of soil organic carbon using on line vis nir Spectroscopy
Computers and Electronics in Agriculture, 2018Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Abstract The selection of samples for modelling of visible and near infrared (Vis-NIR) spectra for prediction of soil organic carbon (OC) is a crucial step for improving model prediction performance. This paper aims at comparing three soil sample selection methods coupled with spiking technique for improving on-line prediction performance of OC. Sample selection methods included random selection (RS), Kennard-Stone (KS) algorithm and similarity analysis (SA). Soil Vis-NIR spectra was measured with an on-line fibre-type Vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. A multiple field sample set (268 samples) was merged with samples (148 samples) collected from one target field, and the resulted sample set was subjected to the three sample selection methods. After dividing spectra into calibration and prediction sets, partial least squares regression (PLSR) was run on the calibration set to develop calibration models for OC, and resulted models were validated using samples of the prediction set. Results show that SA performed generally better than its competitors, especially when there were 58 spiked samples used in the calibration set (54% of total spiked samples of 106), with the best residual prediction deviation (RPD) and root mean squares error of prediction (RMSEP) of 2.14–2.54 and 0.16–0.15% for laboratory and on-line prediction. KS and RS performed similarly, but depending on the size of the calibration set, KS produced slightly better models. This indicates that the proposed SA coupled with spiking holds great potential in the optimization of a calibration set size and may serve as a novel and efficient tool for balancing the cost and quality of vis–NIR calibrations for estimating OC.
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comparison between random forests artificial neural networks and gradient boosted machines methods of on line vis nir Spectroscopy measurements of soil total nitrogen and total carbon
Sensors, 2017Co-Authors: Said Nawar, Abdul Mounem MouazenAbstract:Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (Vis-NIR) Spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, Vis-NIR spectrophotometer (305-2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R²) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R² and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions.
Songchao Chen - One of the best experts on this subject based on the ideXlab platform.
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estimating forest soil organic carbon content using vis nir Spectroscopy implications for large scale soil carbon spectroscopic assessment
Geoderma, 2019Co-Authors: Shangshi Liu, Songchao Chen, Zhou Shi, Haihua Shen, Xia Zhao, Asim Biswas, Xiaolin Jia, Jingyun FangAbstract:Abstract Large-scale soil organic carbon (SOC) stock assessment is expensive as a large number of samples must be collected and then their time-consuming measurements must be made in the laboratory. Previous studies have shown that visible-near-infrared reflectance (Vis-NIR) Spectroscopy can quickly predict SOC content at a low cost. However, the application of this method at the large scale remains challenging due to the high spatial heterogeneity of SOC and the spatially dependent relationships of soil spectra and SOC content. Here, we conducted large-scale soil sampling across China's forests and established the Chinese forest soil spectral library (CFSSL) by measuring SOC content and scanning the Vis-NIR reflectance of 11, 213 soil samples. Compared with the traditional global partial least squares regression (PLSR) modeling method (R2 = 0.75, RPIQ = 1.95), the clustering by fast research and find of density peak in combination with the Cubist model significantly improved the prediction ability of SOC content (R2 = 0.96, RPIQ = 5.83). This study provided a cost-efficient spectroscopic methodology, including measurement and prediction modeling, for large-scale SOC estimation.
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rapid determination of soil classes in soil profiles using vis nir Spectroscopy and multiple objectives mixed support vector classification
European Journal of Soil Science, 2019Co-Authors: Songchao Chen, Shuo Li, W Ji, Dongyun Xu, Guimin ZhangAbstract:Visible‐near infrared (vis–NIR) Spectroscopy can reveal various soil properties and facilitate soil classification. However, few studies have attempted to classify vertical soil profiles that contain several genetic horizons. Here, we propose the ‘multiple objectives mixed support vector classification’ (MOM–SVC) method to classify soil profiles. A total of 130 soil profiles were collected from genetic horizons in Zhejiang Province, China. After laboratory analysis, soil profiles were classified according to the Chinese Soil Taxonomy system. Vis–NIR spectra were recorded from each genetic horizon of each soil profile and were then pre‐processed. We performed the MOM–SVC method as follows: (i) created a support vector machine (SVM) model (one‐versus‐one approach) using spectral data from all soil horizons in calibration profiles, (ii) applied the SVM model on each horizon of the profile to be predicted, (iii) extracted ‘votes’ from each horizon and mixed (or summarized) them into the votes of each profile to be predicted and (iv) classified each profile by the majority‐voting method. We also investigated whether the additional input of auxiliary soil information (e.g. moist soil colour, soil organic matter and soil texture), which could be measured easily or be well predicted by vis–NIR Spectroscopy, could improve the accuracy of soil classification when combined with it. Independent validation results showed that the MOM–SVC method performed better at the soil order level than at the suborder level. Adding auxiliary soil information to the classification model improved the overall accuracy of classification at the soil order level. The proposed MOM–SVC method provides a fast objective diagnostic of soil classes for use in soil surveys and can help to update soil databases when a more objective soil classification system is developed. HIGHLIGHTS: The MOM–SVC method can be used to classify soil profiles objectively with a variety of soil horizons. Stratified random sampling was used to quantify prediction uncertainty in classification MOM–SVC can predict soil orders with greater accuracy than suborders. Adding auxiliary soil information into the classification model improved prediction accuracy.
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in situ measurements of organic carbon in soil profiles using vis nir Spectroscopy on the qinghai tibet plateau
Environmental Science & Technology, 2015Co-Authors: Zhou Shi, Songchao Chen, Lianqing Zhou, R WebsterAbstract:We wish to estimate the amount of carbon (C) stored in the soil at high altitudes, for which there is little information. Collecting and transporting large numbers of soil samples from such terrain are difficult, and we have therefore evaluated the feasibility of scanning with visible near-infrared (Vis-NIR) Spectroscopy in situ for the rapid measurement of the soil in the field. We took 28 cores (≈1 m depth and 5 cm diameter) of soil at altitudes from 2900 to 4500 m in the Sygera Mountains on the Qinghai–Tibet Plateau, China. Spectra were acquired from fresh, vertical faces 5 × 5 cm in area from the centers of the cores to give 413 spectra in all. The raw spectra were pretreated by several methods to remove noise, and statistical models were built to predict of the organic C in the samples from the spectra by partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM). The bootstrap was used to assess the uncertainty of the predictions by the several combinations of pretreat...
Yidan Bao - One of the best experts on this subject based on the ideXlab platform.
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fruit quality evaluation using Spectroscopy technology a review
Sensors, 2015Co-Authors: Hailong Wang, Jiyu Peng, Chuanqi Xie, Yidan BaoAbstract:An overview is presented with regard to applications of visible and near infrared (Vis/NIR) Spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR Spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.
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potential of visible and near infrared Spectroscopy and pattern recognition for rapid quantification of notoginseng powder with adulterants
Sensors, 2013Co-Authors: Pengcheng Nie, Dawen Sun, Fang Cao, Yidan BaoAbstract:Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) Spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rP values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR Spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants.
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visible near infrared spectra for linear and nonlinear calibrations a case to predict soluble solids contents and ph value in peach
Food and Bioprocess Technology, 2011Co-Authors: Yongni Shao, Yidan BaoAbstract:Two sensitive wavelength (SWs) selection methods combined with visible/near-infrared (Vis/NIR) Spectroscopy were investigated to determine the soluble solids content (SSC) and pH value in peaches, including latent variables analysis (LVA) and independent component analysis (ICA). A total of 100 samples were prepared for the calibration (n = 70) and prediction (n = 30) sets. Calibration models using SWs selected by LVA and ICA were developed, including linear regression of partial least squares (PLS) analysis and nonlinear regression of least squares-support vector machine (LS-SVM). In the nonlinear models, four SWs selected by ICA achieved the optimal ICA-LS-SVM model compared with LV-LS-SVM and both of them better than linear model of PLS. The correlation coefficients (r p and r cv), root mean square error of cross validation, root mean square error of prediction, and bias by ICA-LS-SVM were 0.9537, 0.9485, 0.4231, 0.4155, and 0.0167 for SSC and 0.9638, 0.9657, 0.0472, 0.0497, and −0.0082 for pH value, respectively. The overall results indicated that ICA was a powerful way for the selection of SWs, and Vis/NIR Spectroscopy incorporated to ICA-LS-SVM was successful for the accurate determination of SSC and pH value in peach.
Marcia Vernon - One of the best experts on this subject based on the ideXlab platform.
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multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by vis nir Spectroscopy
Journal of Agricultural and Food Chemistry, 2008Co-Authors: Alain Clement, Martine Dorais, Marcia VernonAbstract:Standard methods for determining quality and maturity are time- and labor-consuming and generally measure individual criteria at a specific time, without considering relationships among quality parameters. To propose a rapid and nondestructive analysis method describing multidimensional quality variables, an experiment was undertaken with mature green to overripe tomato fruits found on the North American retail markets. Factor analysis was used to analyze results. Four factors were considered, representing 81% of total variance. The first one, tomato maturity stage (TMS), is related to color, lycopene content, firmness, titratable acidity (TA), pH, and soluble solids (SS). Nondestructive rapid assessment by vis−NIR Spectroscopy can predict TMS (r2 = 0.93). Factors 2 and 3 are both related to taste and should be considered simultaneously. Factor 2, called the gustatory index, is linked to electrical conductivity (EC), SS, TA, and pH. Factor 3, defined by SS, can be directly measured by a refractometer. Fou...
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multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by vis nir Spectroscopy
Journal of Agricultural and Food Chemistry, 2008Co-Authors: Alain Clement, Martine Dorais, Marcia VernonAbstract:Standard methods for determining quality and maturity are time- and labor-consuming and generally measure individual criteria at a specific time, without considering relationships among quality parameters. To propose a rapid and nondestructive analysis method describing multidimensional quality variables, an experiment was undertaken with mature green to overripe tomato fruits found on the North American retail markets. Factor analysis was used to analyze results. Four factors were considered, representing 81% of total variance. The first one, tomato maturity stage (TMS), is related to color, lycopene content, firmness, titratable acidity (TA), pH, and soluble solids (SS). Nondestructive rapid assessment by vis−NIR Spectroscopy can predict TMS (r2 = 0.93). Factors 2 and 3 are both related to taste and should be considered simultaneously. Factor 2, called the gustatory index, is linked to electrical conductivity (EC), SS, TA, and pH. Factor 3, defined by SS, can be directly measured by a refractometer. Fou...
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multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by vis nir Spectroscopy
Journal of Agricultural and Food Chemistry, 2008Co-Authors: Alain Clement, Martine Dorais, Marcia VernonAbstract:Standard methods for determining quality and maturity are time- and labor-consuming and generally measure individual criteria at a specific time, without considering relationships among quality parameters. To propose a rapid and nondestructive analysis method describing multidimensional quality variables, an experiment was undertaken with mature green to overripe tomato fruits found on the North American retail markets. Factor analysis was used to analyze results. Four factors were considered, representing 81% of total variance. The first one, tomato maturity stage (TMS), is related to color, lycopene content, firmness, titratable acidity (TA), pH, and soluble solids (SS). Nondestructive rapid assessment by Vis-NIR Spectroscopy can predict TMS (r(2)=0.93). Factors 2 and 3 are both related to taste and should be considered simultaneously. Factor 2, called the gustatory index, is linked to electrical conductivity (EC), SS, TA, and pH. Factor 3, defined by SS, can be directly measured by a refractometer. Four categories of taste are proposed; the most desirable one ranks high both in soluble solids (above 4.5 degrees Brix) and in gustatory index (above 0). It was not possible to measure the gustatory index by Vis-NIR Spectroscopy (r(2)=0.17), but it can be estimated by EC, using a simple formula. The proposed limit between high and low gustatory index then corresponds to an EC of 5.4 mS/cm. Factor 4, variety, mostly discriminates the pink tomato type and field-grown samples from other varieties.
