Determination Of Soluble Solids Content Research Articles

Determination of soluble solids content in tomatoes with different nitrogen levels based on hyperspectral imaging technique.

Tomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of nitrogen will have different effects on the growth, development, and physiological metabolic processes of tomato, thus affecting the tomato flavor. In this paper, hyperspectral imaging (HSI) technique combined with neural network prediction model was used to predict SSC of tomato under different nitrogen treatments. Competitive adaptive reweighed sampling (CARS) and iterative retained information variable (IRIV) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC are established by custom convolutional neural network (CNN) model that was constructed and optimized. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration. For tomatoes treated with different nitrogen concentrations, the residual predictive deviation (RPD) of CARS-CNN and IRIV-parallel convolutional neural networks (PCNN) reached 1.64 and 1.66, both more than 1.6, indicating good model prediction. This study provides technical support for future online nondestructive testing of tomato quality. PRACTICAL APPLICATION: The CARS-CNN and IRIV-PCNN were the best data processing model. Four customized convolutional neural networks were used for predictive modeling. The CNN model provides more accurate results than conventional methods.

Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy

Soluble solids content (SSC) serves as a crucial metric in assessing loquat quality. To achieve fast, non-destructive, and accurate detection of SSC, the combination of near-infrared (NIR) spectroscopy and broad learning system (BLS) model was developed in this study. Meanwhile, the influence of six spectral preprocessing methods and six wavelength selection algorithms on SSC prediction was explored, and the performance of the BLS model was compared with the widely used partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models. The results showed that Savitzky-Golay smoothing combined with standard normal transformation (SG-SNV) offered the optimal pretreatment, and the hybrid method, namely interval variable iterative space shrinking analysis and successive projections algorithm (iVISSA-SPA), emerged as the most effective wavelength selection method. The BLS model outperformed both PLSR and LS-SVM models. Specifically, the BLS model based on the iVISSA-SPA method achieved the optimal SSC prediction results, with RP2 = 0.8646, RMSEP = 0.5104, and RPD = 2.7481. Therefore, NIR spectroscopy coupled with the BLS model and hybrid wavelength selection strategy could rapidly, non-destructively and accurately detect the SSC of loquat, providing a viable alternative for SSC prediction in fruit.

Visible/near-infrared Spectroscopy and Hyperspectral Imaging Facilitate the Rapid Determination of Soluble Solids Content in Fruits

Visible/near-infrared Spectroscopy and Hyperspectral Imaging Facilitate the Rapid Determination of Soluble Solids Content in Fruits

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Rapid and non-destructive determination of soluble solid content of crown pear by visible/near-infrared spectroscopy with deep learning regression

The development of rapid and non-destructive prediction technology for fruit quality after harvest could enhance market competitiveness and profitability. The soluble solids content (SSC) is an essential quality index of fruit. This study aims to predict the SSC of crown pear using visible/near-infrared (Vis/NIR) spectroscopy (397–1187 nm) with deep learning model MLP-CNN-TCN. Firstly, the spectral information of crown pears was collected, and Savitzky-Golay (SG) smoothing method and Standard normal variate (SNV) were used to preprocess the spectral data. Secondly, the Multi-layer perceptron (MLP) method was used to reduce the dimension of the preprocessed data, and the reduced data was input into a one-dimensional convolutional neural network (1D-CNN) to extract spectral features. Finally, a Temporal convolutional neural network (TCN) was used to establish a regression model to predict pear SSC. This method was also compared with feedforward neural network (FNN), MLP, 1D-CNN, partial least squares regression (PLSR), and support vector regression (SVR). The MLP-CNN-TCN model obtained better prediction performance, with a prediction determination coefficient (RP2) of 0.956 for SSC. This study demonstrated that the combination of Vis/NIR spectroscopy and MLP-CNN-TCN method could rapidly and non-destructively detect SSC of crown pear, and provide a new regression alternative for the prediction of fruit SSC.

Non-destructive determination of soluble solids content in intact apples using a self-made portable NIR diffuse reflectance instrument

The soluble solids content (SSC) is an important internal quality parameter of fruits that is monitored on the fruit market to meet different consumer demands. The aim of this study was to develop a self-made portable near-infrared (NIR) diffuse reflectance instrument to evaluate and monitor the SSC of intact apples. The visible and near-infrared (Vis-NIR) spectra of 118 ‘Fuji’ apples were collected using a Vis-NIR diffuse reflectance spectroscopic measurement system in the spectral range of 450–1100 nm. Savitzky-Golay convolution smoothing and first derivative methods were subsequently applied to eliminate noise interference and baseline shift. Successive projections algorithm (SPA) was performed to extract feature wavelengths from the pretreatment spectra, and the back-propagation artificial neural network (BP-ANN) and multivariate nonlinear regression (MNLR) models were employed to evaluate the predictive ability of the extracted and selected feature wavelengths for the SSC of apples. Seven wavelengths (881, 890, 901, 926, 941, 951, 978 nm) were selected as optimal feature wavelengths from the extracted feature wavelengths (18 wavelengths). The MNLR model (R2 = 0.953, RMSE = 0.391 %) exhibited a high prediction accuracy compared to the BP-ANN model (R2 = 0.865, RMSE = 0.754 %). Based on the results, NIR combined with the MNLR model could be applied in the self-made portable instrument to innovatively monitor the SSC of apples. Finally, a self-made portable NIR diffuse reflectance instrument was designed and developed, and the spectral information of 298 ‘Fuji’ apples was collected using the instrument. According to the results, the MNLR model could well predict the SSC of apples (R2 = 0.871, RMSE = 0.687 %). The overall results also revealed that the developed portable NIR instrument is a promising device for rapid non-destructive monitoring of fruit SSC, thereby meeting the practical application requirement of postharvest commercial processing systems. This instrument could also be beneficial to customers, growers, and producers.

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization.

The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.

Nondestructive determination of soluble solids content, firmness, and moisture content of “Longxiang” pears during maturation using near‐infrared spectroscopy

Nondestructive evaluation of the internal quality of pears during maturation is helpful to instruct production and give advice on harvesting. This study explores the feasibility of predicting the soluble solids content (SSC), firmness, and moisture content (MC) of pears during maturation stage using near-infrared (NIR) spectroscopy. The NIR spectra of 185 “Longxiang” pears were obtained in the wavelength range of 833–2500 nm. Partial least square (PLS) and least squares support vector machine (LSSVM) were used to develop determination models. The LSSVM obtained better performance than PLS, among which the FS-LSSVM model had the best prediction results for SSC (Rp = 0.880, RMSEP = 0.302%, RPD = 2.649, RER = 11.258), firmness (Rp = 0.826, RMSEP = 4.834 N, RPD = 1.345, RER = 5.772), and MC (Rp = 0.872, RMSEP = 0.570%, RPD = 2.281, RER = 6.842). This study demonstrates that NIR spectroscopy technique can be applied for nondestructive detection of SSC, firmness, and MC of pears during maturation. Practical applications Postharvest quality of fruit depends largely on the maturity degree and internal quality parameters at harvest. Soluble solids content, firmness, and moisture content (MC) are the most essential internal quality indicators, which directly determine pears’ unique taste and purchase decision of consumers. For this, near-infrared spectroscopy was simultaneously applied to determine soluble solids content, firmness, and MC of pears during maturation stage, and FS-LSSVM model had the best prediction results. The result of this study is helpful to instruct pear production and give advice on harvesting.

Determination of soluble solid content in apples using near infrared spectroscopy

Determination of soluble solid content in apples using near infrared spectroscopy

Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: comparison of Partial Least Square (PLS) and Principle Component Regression (PCR)

Determination of soluble solid content (SSC) and pH of banana was investigated using a modular Vis/NIR spectroscopy in reflectance mode. Vis/NIR spectroscopy has been applied for non-destructive SSC or pH measurement, but limited studies were conducted for a modular VIS/NIR spectroscopy. This study was conducted to develop a calibration model to predict SSC and pH in bananas using a modular type of VIS/NIR spectroscopy at wavelength of 350-1000 nm. Two chemometrics analysis namely partial least square (PLS) and principle component regression (PCR) were used to develop calibration models and to predict SSC and pH of bananas. Normalization, baseline correction, standard normal variate (SNV), and multiplicative scatter correction (MSC) pre-processing were used for spectra transformation. Research showed that PLS regression produced better models compared to PCR in determining SSC and pH contents. PLS regression resulted in RC 2 of 0.95, RMSEC of 1.27, Rp 2 of 0.85, RMSEP of 1.98, and bias of -0.09 for SSC and RC 2 of 0.96, RMSEC of 0.05, Rp 2 of 0.82, RMSEP of 0.11, and bias of 0.11 for pH. PCR resulted in RC 2 of 0.78, RMSEC of 2.63, Rp 2 of 0.76, RMSEP of 2.71, and bias of -0.12 for SSC and RC 2 of 0.71, RMSEC of 0.14, Rp 2 of 0.62, RMSEP of 0.16, and bias of -0.02 for pH. This modular Vis/NIR instrument combined with proper pre-processing method and chemometric analysis is promising to be used for determination of SSC and pH of fruits.

Determination of soluble solid content in market tomatoes using near-infrared spectroscopy

Tomatoes are widely consumed worldwide, and the soluble solid content (SSC) is one of the most important quality parameters for the commercialization of fresh tomatoes, mainly in the salad group. In this regard, partial least square models for intact tomatoes SSC were developed using a portable F-750 Vis-near-infrared (NIR) with Zeiss MMS1-NIR spectrometer in an interactance geometry. Thus, tomatoes from five regions (states of Goiás, Bahia, Santa Catarina, Minas Gerais, and São Paulo) were collected weekly from November 2018 to November 2019, with a total sample number of 2.085, divided into three populations, two for calibration and one for prediction. The best partial least squares regression prediction model was obtained using the Vis-NIR spectral region of 840–1050 nm with Orthogonal Signal Correction (OSC) pre-treatment applied. The calibration population standard deviation (SD) was 0.52%, and for the prediction population, the SD was 0.56%. Low root mean square error cross-calibration of 0.32%, and root mean square error prediction of 0.32% were achieved. The models were able to discriminate low from high values and vice versa.

Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy

Abstract Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.

Determination of soluble solids content and firmness in plum using hyperspectral imaging and chemometric algorithms

AbstractHyperspectral imaging technology coupled with chemometric algorithms was investigated to determine soluble solids content (SSC) and firmness in plum. A model of determining SSC and firmness in plum was established and optimized. Hyperspectral images of two varieties of “Red” and “Green” plums were acquired by hyperspectral imaging acquisition system. Two methods of partial least square regression (PLSR) and principal components regression (PCR) were employed to establish models based on full spectra. The multiple linear regression (MLR) and error back propagation (BP) network were applied to establish simplified models based on characteristic spectra selected by the methods of successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS). By comparing the results of different modeling methods, CARS‐MLR model was the optimal models for predicting SSC and firmness with R2 P greater than 0.9 and 0.6, RPD above 3.7 and 1.8. Especially, for SSC (R2 P = 0.93, RMSEP = 0.57%, RPD = 3.73), for firmness (R2 P = 0.69, RMSEP = 0.63 kg/cm2, RPD = 1.81). Results showed that hyperspectral imaging technology coupled with chemometric algorithms is feasible to determine SSC and firmness, CARS‐MLR model was the optimal models.Practical applicationsSoluble solids content (SSC) and firmness are two significant quality attributes that determine fruit maturity and grade after harvest. Traditional methods for determining SSC and firmness are destructive, time‐consuming, and extremely affected by subjective factors. Hyperspectral imaging technology has the advantages of nondestructive, rapid, non‐pollution, and so on. The results demonstrated that hyperspectral imaging technology coupled with chemometric algorithms is feasible to determine SSC and firmness, and it can provide a theoretical basis to develop a real‐time detection system to determine the quality of fruits.

A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer.

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

Nondestructive determination of soluble solids content and pH in red bayberry (Myrica rubra) based on color space.

Color has strong relationship with food quality. In this paper, partial least square regression (PLSR) and least square-support vector machine (LS-SVM) models combined with six different color spaces (NRGB, CIELAB, CMY, HSI, I1I2I3, and YCbCr) were developed and compared to predict pH value and soluble solids content (SSC) in red bayberry. The results showed that PLSR and LS-SVM models coupled with color space could predict pH value in red bayberry (r = 0.93-0.96, RMSE = 0.09-0.12, MAE = 0.07-0.09, and MRE = 0.04-0.06). In addition, the minimum errors (RMSE = 0.09, MAE = 0.07, and MRE = 0.04) and maximum correlation coefficient value (r = 0.96) were found with the PLSR based on CMY, I1I2I3, and YCbCr color spaces. For predicting SSC, PLSR models based on CIELAB color space (r = 0.90, RMSE = 0.91, MAE = 0.69 and MRE = 0.12) and HSI color space (r = 0.89, RMSE = 0.95, MAE = 0.73 and MRE = 0.13) were recommended. The results indicated that color space combined with chemometric is suitable to non-destructively detect pH value and SSC of red bayberry.

Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models

Abstract The feasibility of using visible and near infrared full transmittance hyperspectral imaging for predicting soluble solids content (SSC) in oranges has been assessed. A combination of competitive adaptive reweighted sampling and successive projections algorithm (CARS-SPA) was used to select the effective wavelengths. Size of fruit was used as a compensation factor to establish a calibration model coupled with spectral information. Full transmittance spectra and physiochemical parameters (SSC and size) of samples were extracted. The potential outliers in samples were eliminated by Monte-Carlo outlier detection method. Effective wavelengths were selected by CARS algorithm and the newly proposed CARS-SPA combination method. Three types of models including partial least squares (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) were established for SSC analysis of fruit based on different inputs. Results indicated that all models can realize the satisfactory prediction of SSC in oranges. Ranges of coefficient of determination ( R p r e 2 ) and root mean square error of prediction (RMSEP) were 0.88-0.89 and 0.48-0.48 % for PLS models, 0.83-0.85 and 0.49-0.55 % for MLR models, 0.86-0.90 and 0.40-0.48 % for LS-SVM. Compared among all SSC analysis models, CARS-SPA was a powerful effective wavelength selection combination and CARS-SPA-LS-SVM model with size had the optimal prediction accuracy ( R p r e 2 = 0.90, RMSEP = 0.40, RPD = 3.18). Overall, the results revealed that full transmittance hyperspectral imaging can be used to non-invasively to rapidly measure the SSC of oranges. A robust and accurate model could be established based on CARS-SPA-LS-SVM method with size compensation. These results may provide a useful reference for assessment of other internal quality attributes, such as acidity, of the thick-skinned fruit.

Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods

Aim of study: Developing models to determine soluble solids content (SSC) in cherry trees by means of Vis/NIR spectroscopy.Area of study: The Spanish Autonomous Community of Aragón (Spain).Material and methods: Vis/NIR spectroscopy was applied to Prunus avium fruit ‘Chelan’ (n=360) to predict total SSC using a range 400-2420 nm. Linear (PLS) and nonlinear (LSSVM) regression methods were applied to establish prediction models.Main results: The two regression methods applied obtained similar results (Rcv2=0.97 and Rcv2=0.98 respectively). The range 700-1060 nm attained better results to predict SSC in different seasons. Forty variables selected according to the variable selection method achieved Rcv2 value, 0.97 similar than full range.Research highlights: The development of this methodology is of great interest to the fruit sector in the area, facilitating the harvest for future seasons. Further work is needed on the development of the NIRS methodology and on new calibration equations for other varieties of cherry and other species.

The overall precision test for near infrared scanning and reference method for the determination of soluble solids content and pH of mango for processing factory

Near infrared (NIR) spectroscopy is a rapid technique for nondestructive testing. Mango is popular fruit in Thailand. Therefore, The main aim of this paper is to report an overall precision of the NIR spectroscopy instruments and reference methods for determination at the beginning of the experiment for prediction models development to be in the mango applied processing factory.. Results showed that the repeatability of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00191 and 0.00529, respectively. The reproducibility of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00323 and 0.03561, respectively. Repeatability of reference test of TSS and pH were 0.1657 and 0.0827. Therefore, the R2max of TSS and pH were 0.9825 and 0.9504 which indicates that it is possible to develop NIR model for prediction of total soluble solids and pH.

Non-destructive determination of soluble solids content using a multi-region combination model in hybrid citrus

Fruit is complicated natural production and the chemical components are heterogeneous at different position, the comprehensive quality evaluation for intact fruit is thefairest way for avoiding the nonuniform distribution. In order to build a higher accuracy model for soluble solids content (SSC) evaluation, the visible/near infrared spectra of citrus were collected from stem, equator and navel positions to analyze the influence of spectrum measurement position on the prediction accuracy of SSC. The SSC value gradually reduced from navel to equator and stem positions by sequence, but the trend of peel thickness of these three regions were opposite of SSC. The coefficientcorrelation of SSC between intact fruit and three local regions reached remarkablelevel, which proved it was feasible to build an accurate model for SSC prediction of intact fruit using the spectral information of local positions. Then separate local models based on specific position spectrum (stem, equator and navel) were built, the result showed the equator position was more suitable to evaluate SSC of intact fruit than navel and stem positions due to the better prediction accuracy, however the unpredictability and variation of the spectral collection position is a challenge to the prediction ability of intact fruit quality. Next multi-region combination models that fusing spectral information of multiple positions were developed, the combination models of ‘Equator + Navel’ and ‘Stem + Equator + Navel’ achieved optimal performance than other combination models and all separate local models, with the correlation coefficient of prediction set (Rpre) and root mean square error of prediction (RMSEP) of 0.8507, 0.8424 and 0.6015°Brix, 0.5901°Brix, respectively. It indicated that the peel thickness interfered the acquisition of spectral information of flesh layer, but the accuracy and robust of the prediction model could be improved through fusing the spectral information of multiple regions. Therefore, the fusion of multi-information sets should be deserved more attention to build a practicable model that is not sensitive to the variation of spectrum measurement position for SSC evaluation of intact citrus fruit.

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy

Apple is one of the most popular fresh fruits with an extensive scope of regions owing to its nutrition and sweet in flavor. There is a large difference in the composition of the fruits growing in varying regions because of the variation in the growing regions, such as temperature, soil nutrients, etc. As a result, it is of significance to decrease the impact of region variability on the measurement of soluble solids content (SSC) in apples. To lessen the impact of region variability and enhance the predictive ability of on the model, our manuscript compared the performance of the two multi-region prediction models for the estimation of SSC in apples from multiple geographical regions. One multi-region prediction model was developed by merging SSC values and spectral data of all samples from multiple regions. The other multi-region prediction model was built for the determination of SSC in combination with region discriminant, model search strategy, and single-region models. Support vector machine (SVM) was applied to establish the model for discriminating the apples from multiple geographical regions. It was found that the region discriminant model achieved great results, with the classification accuracy of 99.52%. By comparing and analyzing the two multi-region prediction models, the optimal multi-region prediction model was obtained. Finally, to decrease the irrelevant spectral information and reduce the computational cost, the multi-region SSC prediction model was optimized in combination with various spectral preprocessing methods (multiple scatter correction (MSC), standard normal variate (SNV), and first derivative (FD) correction) and variable selection methods (Monte Carlo uninformative variables elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF)). The overall results denoted that it was more accurate to estimate SSC in apples from the different geographical regions by using the multi-region models based on the region discriminant model in combination with SNV preprocessing algorithm and MC-UVE variable selection algorithm, and the prediction accuracy preceded the single-region models.