Standard Error Of Calibration Research Articles

The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics.

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

Rapid and non-destructive quality estimation of cinchona, andrographis paniculata, and black pepper using a portable NIR spectroscopy measuring device

Quality assessment of natural products are mostly done by laboratory methods which are invasive, cost-intensive, with low-throughput and prone to human error. Near infrared (NIR) spectroscopy have emerged as a very useful technique as a rapid non-destructive detection and quality estimation tool in identifying chemical fingerprint sensitive to the vibrational modes in chemical bonds like O-H, C-H and N-H.In this work, a reasonably low-cost and portable NIR spectrometer was developed with a few imported internal components, together with compatible software for its use, like the graphical user interface (GUI) and calibration program. Quinine in Cinchona (Cinchona L) bark, andrographolides in Andrographis paniculata and piperine in black pepper were estimated and the coefficient of determination (R2) of 0.93, 0.94, and 0.90 was obtained respectively for the regression analysis. The model for cinchona, andrographolides, and piperine gave residual prediction deviation (RPDs) of 4.25, 4.21 and 3.0, range error ratio (RERs) of 14.59, 14.42, 10.32 and the ratio between the standard error of calibration (SEC) and the standard error of prediction (SEP) i.e. SEP/SEC of 1.18, 0.21 and 0.92 respectively. The derived partial least squares (PLS) regression models fulfil the requirements of AACC Method 39–00 (AACC in AACC Method, 39–00:15, 1999) used for screening (RPD ≥ 2.5), as per knowledge of the authors. The device can be used for other molecular markers in plants and their byproducts.

Direct Determination of Ca, K, and Mg in Soy Leaf Samples Using Laser-Induced Breakdown Spectroscopy.

This study was dedicated to developing analytical methods for determining macronutrients (Ca, K, and Mg) in soy leaf samples with and without petioles. The study's primary purpose was to present Laser-induced breakdown spectroscopy (LIBS) as a viable alternative for directly analyzing leaf samples using chemometric tools to interpret the data obtained. The instrumental condition chosen for LIBS was 70 mJ of laser pulse energy, 1.0 µs of delay time, and 100 µm of spot size, which was applied to 896 samples: 305 of soy without petioles and 591 of soy with petioles. The reference values of the analytes for the proposition of calibration models were obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES) technique. Twelve normalization modes and two calibration strategies were tested to minimize signal variations and sample matrix microheterogeneity. The following were studied: multivariate calibration using partial least squares and univariate calibration using the area and height of several selected emission lines. The notable normalization mode for most models was the Euclidean norm. No analyte showed promising results for univariate calibrations. Micronutrients, P and S, were also tested, and no multivariate models presented satisfactory results. The models obtained for Ca, K, and Mg showed good results. The standard error of calibration ranged from 2.3 g/kg for Ca in soy leaves without petioles with two latent variables to 5.0 g/kg for K in soy leaves with petioles with two latent variables.

Identification of Variety and Prediction of Chemical Composition in Cocoa Beans (Theobroma cacao L.) by FT-MIR Spectroscopy and Chemometrics.

Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < R2c < 0.98 (R2c: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < R2v < 0.97 (R2v: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.

Simultaneously determining amino acid contents using near-infrared reflectance spectroscopy improved by pre-processing method in rice

Rice, the most consumed in Asian and constitute important source of amino acids. The present study evaluated the suitability of near-infrared reflectance spectroscopy to predict amino acid contents (AACs) using 1210 rice samples representing a wide range of grain qualities and genetic background. Calibration models established by combining different spectral derivatives, scatter and baseline correction methods, and regression algorithm were compared using full cross validation. A modified partial least square model (MPLS) with the spectral derivative treatment “2, 3, 3, 1” (2nd order derivative computed based on 3 data points, and 3 and 1 data points in the 1st and 2nd smoothing, respectively) and weighted multiplicative scattering correction (WMSC) method was identified as the best model for simultaneously measurement of 14 AACs in brown rice flour. The high model quality is reflected by RSQ of 0.914∼0.969, standard error of calibration (SEC) of 0.0187%∼0.0553%, 1-VR of 0.901∼0.955, SECV of 0.0214%∼0.0579%, and residual prediction deviation (RPD) of 2.58∼3.85. These results indicated that NIRS coupled with an appropriate pre-processing, multivariate regression could be a rapid and reliable method for quantification of AACs in rice breeding programs and quality control in the food industry. Factors that would influence model quality were investigated and discussed.

Rapid and non-invasive estimation of total polyphenol content and antioxidant activity of natural corks by NIR spectroscopy and multivariate analysis

The use of near infrared spectroscopy (NIRS) coupled with multivariate data analysis has been evaluated for the rapid and non-invasive estimation of total polyphenol content (TPC) and antioxidant activity of natural corks. A total of 132 samples were used, using two-thirds and one-third of spectra for calibration and external validation sets, respectively. Regression models were developed by partial least squares (PLS) regression analysis. The best models were achieved by different spectral preprocessing approaches with 7 latent variables for TPC and 9 for antioxidant activity, a variance explained by the models between 64.7 % and 72.0 %, and a standard error of calibration (SEC) between 3.6 and 6.8, respectively. Good relationships between chemical values measured by conventional assays and NIRS predicted values were obtained, with standard error of prediction (SEP) of 5.0 and 5.8 for ABTS and DPPH antioxidant activity, and 5.8 for TPC. Regression lines for calibration and external validation presented good correlation coefficients (r2) from 0.80 to 0.85, and from 0.62 to 0.76, respectively. The models obtained showed that it is possible to estimate rapidly and simultaneously, the TPC and antioxidant activity of natural corks by combining the NIRS with different multivariate analysis tools.

Prediction of sorghum oil content using near‐infrared hyperspectral imaging

Background and Objectives Aside from being a staple crop, sorghum is now being used as a gluten-free food, an animal feed ingredient, and a biofuel source. This growing demand for sorghum has increased interest in grain quality and utilization. This study explored near-infrared hyperspectral imaging (NIR HSI) as a non-destructive and rapid method to predict the oil content of sorghum grains. Findings Partial least square (PLS) regression models for oil from NIR HSI spectra achieved 0.19% standard error of calibration (SEC) and 0.21% standard error of prediction (SEP) at 10 PLS factors. The results from the NIR HSI instrument were comparable to those from the single-kernel near-infrared reflectance instrument (SKNIR) using the same set of samples. Conclusion This study showed the potential of hyperspectral imaging as a quality control method for sorghum grains, specifically for oil content, which could be beneficial for sorghum breeders, growers, and processors. Significance and Novelty The increasing interest in sorghum use prompted this study which is one of the first to explore NIR hyperspectral imaging for sorghum oil with the ability to indicate single seed weight. This article is protected by copyright. All rights reserved.

Prediction of Acoustic Velocity Properties of Downed Pine Trees Using Near-Infrared Spectroscopy

AbstractNear-infrared reflectance (NIR) spectroscopy was used to determine correlations between acoustic velocity and stiffness properties of downed pine trees in the southern coastal plains of the United States. Three acoustic measurement methods (longitudinal, transverse, and offset) were used. From the measurement of the acoustics, the time of flight (TOF) was determined from the downed trees. Increment core samples were obtained from each thirty downed pine trees in the study. NIR spectra were obtained using a fiber probe on the radial surface of each core to rapidly correlate the speed of sound, estimate the strength properties of the downed trees, and the TOF acoustic assessments. The NIR prediction was very good for the transverse and offset methods. The predictability diagnostic was above an R2 of 0.70 for both offset measurements for the transverse methods for the acoustic velocity and dynamic modulus of elasticity (MOE). The longitudinal measurement exhibited the weakest model (R2 &amp;lt; 0.65) for both the acoustic velocity and the MOE with the highest standard error of prediction between 3.0 (ELVLSWV) and 0.31 (VLSWV) for the three measurement types. All the standard errors of calibration were below 1% except in ELVOSWV, which was ∼2%. The dry density measured from the increment cores had a moderate correlation (R2 ∼ 60%), compared with the lower correlation (R2 ∼ 50%) by the green density in the multiple linear regression output. The results of the acoustic model indicated that NIR spectroscopy has the potential to predict the acoustic velocity and corresponding stiffness of downed trees.

Investigating the Influence of Grain Variety on Calibration of Microwave Moisture Sensors

<b>Highlights</b> <list list-type=bullet><list-item> Dielectric properties of wheat and corn were measured at 10 GHz and 25°C. </list-item><list-item> Moisture content was determined with a dielectric-based density-independent calibration function. </list-item><list-item> No Influence of variety in calibrating microwave moisture sensors was observed for wheat and corn. </list-item><list-item> A unified calibration for moisture determination in wheat and corn provided moisture content with SEC of 0.87%. </list-item></list> <b>Abstract.</b> Microwave moisture sensors represent a sound solution for real-time, nondestructive determination of moisture content in grains, seeds, nuts, and other agricultural products. One of the most critical phases in developing these sensors is their calibration against standard methods, in this instance, the oven-drying technique. In this study, dielectric properties of different varieties of wheat, and corn were measured at 10 GHz and an average room temperature of 25.2°C to investigate the influence of variety in calibrating microwave moisture sensors. Dielectric properties measured for all varieties tested for a given material were nearly superimposed, and the same was observed when plotting density-independent calibration function, , as a function of moisture content. Performance of individual moisture calibration equations in predicting moisture content was compared to that from a single moisture calibration equation established for all varieties combined through computation of the standard error of calibration (SEC). In most instances, for both wheat and corn the SEC corresponding to individual varieties were nearly the same as that corresponding to all varieties combined. In addition, it was observed that a unified calibration can be established for both wheat and corn whereby moisture can be predicted from a single moisture calibration equation based on measurement of the dielectric properties at a single microwave frequency. Here, such an equation provided moisture content in wheat and corn with a SEC of 0.87%.

A predictive model for the determination of cadmium concentration in cocoa beans using laser-induced plasma spectroscopy

This study proposes a predictive model to determine the concentration of cadmium (Cd) in cocoa beans based on laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR-1 or PLS-1). The multivariate calibration model was developed using 46 cocoa bean samples, with Cd concentrations up to 1 mg kg-1. The increase of the LIBS signal in the Cd emission lines was evident when the cocoa bean sample was subjected to a solid-liquid-solid transformation (SLST). The range error ratio (RER) was 7.92, which allowed it to be classified as a screening model. Monte Carlo cross-validation was used, with 60% of samples for calibration and the remaining for testing. The standard error of cross-validation (SECV) and standard error of calibration (SEC) were 0.12 mg kg-1 and 0.05 mg kg-1, respectively. The proposed procedure is framed within the alternatives for the chemical analysis of cocoa.

A high throughput phenotyping technique for banana cultivar Sukali Ndizi based on internal fruit quality attributes

Background: Sukali Ndizi quality traits such as Total soluble solid (TSS) content, pulp texture and sugar/acid (S/A) ratio are critical in quality assessment. Screening very large numbers of fruit genotypes has prompted the development of a high throughput method using Near Infrared spectrometry (NIRS). Results: The calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure. Calibration equations for all analytical characteristics were computed by NIR Software ISI Present WINISI using Modified Partial Least Squares (MPLS) and Partial Least Squares. The quality of calibration models were evaluated by Standard Error of Calibration and coefficient of determination parameters between the measured and the predicted values. The results obtained with FOSS NIR systems 2500 spectrometer (model DS 2500) using the 350-2500 nm range, showed good prediction of the quality traits TSS content, pulp texture and S/A ratio. The MPLS method produced satisfactory Calibration model performance for TSS, texture and S/A ratio, with typical Rc2 of 0.73%Brix, 0.69kgf and 0.7; and root mean squared standard error of calibration of 0.73%Brix, 0.25kgf and 5.36 respectively. This is a good set of quality traits predicting Sukali Ndizi quality with NIRS with robustness, as it was obtained by using diverse Ndizi populations. Conclusions: This can be a useful tool to phenotype large numbers of Ndizi hybrids per day, making it possible to reduce on the resources spent when utilizing organoleptic evaluation selection technique.

Use of dielectric properties measurements at microwave frequencies for real-time monitoring of water activity in almonds

A dielectric-based method for real-time tracking of changes in water activity in almond kernels is proposed. The method is based on measurement of the dielectric properties at a single microwave frequency. Samples of almond kernels (Butte/Padre and Nonpareil) were placed in a chamber with high humidity and their dielectric properties were measured at 10 GHz and room temperature (22 °C) every few hours. The dielectric properties increased linearly with time and exponentially with water activity. A three-parameter exponential growth equation provided relationships correlating the dielectric constant (ε’), the dielectric loss factor, (ε”), and the loss tangent (ε”/ε’) with water activity with high coefficients of determination (r2 ). From these equations, water activity calibration equations were obtained. Performance of these equations in predicting water activity under dynamic situations were assessed by calculating the standard error of calibration (SEC). Values of SEC ranged from 0.01 to 0.05.

Simple and effective mathematical models for cellulose water content calculation from absorbance/wavenumber shifts in NIR spectrum

This study presents the evaluation of the near infrared (NIR) spectroscopy utility in the quantitative analysis of the moisture content in cellulose (from 0.5 to 11.0 wt.%). Being the first statistic-based work examining the relations between the wavenumber/absorbance and moisture content, this research innovatively describes the variations in the X- and Y-coordinates of 9 absorption bands plotted as a function of water amount established with Karl-Fischer titration. Simple linear, semilogarithmic and power models have been investigated. While model exhibited R2>90%, the standard error of calibration (SEC) has been shown and an external validation was carried out. Power model has been successfully applied to describe the changes in the intensity of the peak situated from 5400 to 5000 cm−1 (R2 = 90%) with a standard error of prediction of approx. 17.2%. Furthermore, absorption bands that wavenumber shifts could provide significant information on the behaviour of the active centres in cellulose have been favourably defined.

Original study on mathematical models for analysis of cellulose water content from absorbance/wavenumber shifts in ATR FT-IR spectrum

The aim of this research was to evaluate the applicability of the attenuated total reflectance Fourier-transform infrared (ATR FT-IR) spectroscopy in the quantitative analysis of the moisture content in cellulose (from 0.5 to 11.0 wt.%). Innovatively, this work describes the variations in both absorbance and wavenumber of 16 absorption bands plotted as a function of cellulose water amount measured with Karl-Fischer titration. Different regression models were investigated (simple linear, semilogarithmic, power) and the adjusted coefficient of determination (R2) was given for each calculation. While model exhibited R2 > 90%, the standard error of calibration (SEC) was presented and an external validation has been performed. Regarding the absorbance-water content relationship, data recorded for sixteen peaks was successfully fitted with linear functions exhibiting R2 > 90%. The highest value of R2 = 98.7% and standard error of prediction SEP = 0.3wt.% have been assigned to the maximum from 3339 to 3327 cm−1 (–OH), proving ATR FT-IR usefulness in quantitative analysis.

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm−1 was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R2), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm−1 was suitable for modeling (calibration sets: validation set = 4:1), the R2, SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method.

Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics.

In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.

Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.

Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13197-022-05456-7.

Design of an Integrated Near-Infrared Spectroscopy Module for Sugar Content Estimation of Apples.

An integrated near-infrared (NIR) spectroscopy prototype module for sugar content estimation of apples is presented. Since this is the first attempt to design an integrated NIR spectroscopy module, we followed the design process as follows. First, we estimated the sugar content of apples using a tungsten halogen light source and a 700 nm–1000 nm NIR spectrometer with a 10 nm wavelength resolution and a 16b analog-to-digital converter (ADC) resolution. Second, we determined the most effective wavelengths among 31 evenly distributed wavelengths while observing the correlation coefficient, R2, and then we reduced the ADC resolution 1b by 1b starting from 16b while also observing the R2. Lastly, we designed an integrated NIR spectroscopy module with the selected eight wavelengths and a 13 ADC resolution. The module implemented in a 0.18 μm 1P6M CMOS process occupies a die area of 0.84 mm2. By utilizing this module with eight off-chip light emitting diodes (LED) and one photo diode (PD), the measured R2 and the standard error of calibration (SEC) were 0.365 and 0.686 brix, respectively.

Nondestructive Quantitative Analysis of Water Potential of Tomato Leaves Using Online Hyperspectral Imaging System

HighlightsWe developed an online measurement system for water potential of tomato plants using hyperspectral imaging.We used Linear Discriminant Analysis to automatically and quickly extract the leaf images.We used SNV scattering correction to remove the spectral variations caused by collecting the defocused leaf images.We developed a prediction model of leaf water potential based on spectral image information.Abstract. Tomatoes have different water requirements in each growing period. Excessive water use or insufficient water supply will affect the growth and yield of tomato plants. Therefore, precise irrigation control is necessary during cultivation to increase crop productivity. Traditionally, the soil moisture content or leaf water potential has been used as an indicator of plant water status. These methods, however, have limited accuracy and are time-consuming, making it difficult to be put into practice in tomato production. This study developed an online hyperspectral imaging system to measure the leaf water potential of tomato nondestructively. Linear Discriminant Analysis was utilized to automatically and quickly extract the leaf images, with the recognition accuracy of 94.68% was achieved. The mathematical processing of Standard Normal Variate scattering correction was used to remove the spectral variations caused by the defocused leave images. The developed leaf water potential prediction model based on the spectral image information attained using the developed system achieved the standard error of calibration of 0.201, coefficient of determination in calibration set of 0.814 and standard error of cross-validation of 0.230, and one minus the variance ratio of 0.755. The obtained performance indicated the feasibility of applying the developed online hyperspectral imaging system as a real-time non-destructive measurement technique for the leaf water potential of tomato plants. Keywords: Hyperspectral imaging system, Machine learning, Tomato, Water potential.