Apple Soluble Solids Content Research Articles

Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy

Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with Rp, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.

Postharvest acibenzolar-S-methyl treatment maintains storage quality and retards softening of apple fruit.

"Golden delicious" apples were dipped in 100mg/L acibenzolar-S-methyl (ASM) to investigate the fruit quality and softening during 12days of storage. Weight loss, flesh firmness, ethylene release, respiratory rate, content of total soluble solids and titratable acid, the activity of pectinase, cellulase, and β-glucosidase, and water-insoluble pectin and water-soluble pectin contents were investigated. The results demonstrated that ASM treatment inhibited ethylene release and respiratory rate, reduced titratable acidity, and enhanced total soluble solids content in apples. Moreover, application of ASM suppressed the reduction of flesh firmness, activity of pectin methylesterase, and polygalacturonase. Cellulase, β-glucosidase, and degradation of protopectin in apple fruit were also suppressed by ASM treatment during storage. In conclusion, ASM could maintain fruit quality by regulating cell wall-degrading enzymes during storage. PRACTICAL APPLICATIONS: Application of acibenzolar-S-methyl after harvest has the potential of delaying fruit softening by regulating cell wall-degrading enzymes, thus retain fruit quality.

Optical sensing system for detection of the internal and external quality attributes of apples

Abstract An optical sensing system for the detection of surface bruises and the internal qualities of apples has been developed. Isohypse line extraction combined with marker constraint watershed segmentation (ILE-WSM), as a method to resolve the uneven brightness problem in apple images during bruise detection was investigated. The method has three steps: first, morphological filtering to reduce the random noise in the raw images; second, the ILE to locate the bruise position in the de-noised images; and finally, the WSM to complete the final image segmentation. For a 300 undamaged and bruised apples, the correct classification rate was 97.3 % using the ILE-WSM method, showing better segmentation ability than the Otsu method. For internal quality detection, the normalized spectral ratio (NSR) method has been proposed to correct the light scattering effects in the raw spectra. The NSR has the advantages of a simple calculation and high precision over the other methods. The final detection models for the apple soluble solids content (SSC) and dry matter content (DMC) were built on the key variables after selection by the competitive adaptive reweighted sampling (CARS) method. The root mean square error of the prediction dataset (RMSEP) and the correlation coefficient of the prediction dataset (Rp) of the final model prediction for the SSC and DMC were 0.412 % and 0.957 and 0.602 % and 0.937, respectively. The size of the whole system was 1600 mm × 500 mm × 1500 mm and the total time required to inspect each apple was 0.42 s. The optical sensing system can successfully be applied to apple surface bruise and internal quality detection.

Nondestructive measurement of soluble solids content in apple using near infrared hyperspectral imaging coupled with wavelength selection algorithm

Hyperspectral imaging is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models to extract effective wavelengths for measurement of apple soluble solids content (SSC) based on near infrared (NIR) hyperspectral imaging over the spectral region of 1000–2500 nm. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), random frog (RF), and CARS-SPA, CARS-RF combined algorithms were used for extracting effective wavelengths from hyperspectral images of apples, respectively. Based on the selected effective wavelengths, different models were built and compared for predicting SSC of apple using partial least squares (PLS) and least squared support vector regression (LS-SVR). Among all the models, the models based on the ten effective wavelengths selected by CARS-SPA achieved the best results, with Rp, RMSEP of 0.907, 0.479 °Brix for PLS and 0.917, 0.453 °Brix for LS-SVR, respectively. The overall results indicated that CARS-SPA can be used for selecting the effective wavelengths from hyperspectral data. Both PLS and LS-SVR can be applied to develop calibration models to predict apple SSC. Furthermore, the wavelengths selected by CARS-SPA algorithm has a great potential for online detection of apple SSC.

Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method

The long-term performance of a near-infrared (NIR) calibration model for soluble solids content (SSC) prediction has been investigated using apples with biological variability collected from 2012 to 2018. The NIR spectrum in the range of 4000–10,000 cm−1 was acquired around equator position for each sample. Partial least squares (PLS) was used to develop calibration model based on the samples harvested in 2012 and 2013. The model was then applied to predict the SSC of samples in five separate data sets collected from 2014 to 2018, resulting in a lower performance with higher RMSEP values in the range of 0.704–1.716%. After applying the slope and bias (S/B) correction method, ten samples were selected from each prediction set and used to adjust the model; the prediction results for five independent prediction sets were improved, with RMSEP values ranging from 0.501% to 0.654%. Subsequently, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) methods were used to select the most effective wavelengths for the determination of SSC. The calibration model built with 15 wavelengths, combined with the S/B correction method, could replace the full spectral range to detect the SSC of apples over a long period of time, with Rp and RMSEP for five prediction sets being 0.919, 0.937, 0.908, 0.896, 0.924 and 0.592, 0.637, 0.513, 0.523, 0.500%, respectively. Overall, the proposed method in this study could make the model valid and robust over a long time and make the biological variability a negligible interference for SSC prediction, thereby providing potential for SSC prediction in practical application.

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.

基于光谱技术的Bipls算法结合CARS算法的苹果可溶性固形物含量检测

基于光谱技术的Bipls算法结合CARS算法的苹果可溶性固形物含量检测

Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system

Near infrared spectroscopy has been widely applied in the area of rapid determination for fruits’ internal qualities. Therefore, an on-line near-infrared detection system was established to predict the SSC of apples in this study. Due to random measurement positions of apples, negative influences will be exerted on the performance of prediction models. With the aim of learning more about these influences and also compensating for them, spectra were taken at different measurement positions in the present work, including six fixed positions and a random position. Besides, the relations between these positions were investigated as well. It is also found that when the concave surfaces at apple’s calyx and stem interfered with the light path in the detection system, model’s robustness and accuracy would be deteriorated. At last, average and global spectra were used to build prediction models with comparison purpose. The optimal prediction model was established by the average spectra of seven measurement positions (RMSEC 0.356%; rc 0.947; RMSEP 0.370%; rp 0.906), which was superior to results in many previous studies. Last but not least, several suggestions on compensation for these influences were made to improve the model performance in some practical applications.

Effect of fruit moving speed on online prediction of soluble solids content of apple using Vis/NIR diffuse transmission

AbstractThe effect of fruit moving speed on online prediction of soluble solids content (SSC) of “Fuji” apples based on visible and near‐infrared (Vis/NIR) spectroscopy was studied. Diffuse transmission spectra between 615 and 1,045 nm were collected with a commercial online system at speeds of 0.3 m/s (S1), 0.5 m/s (S2), and 0.7 m/s (S3). Compensation models for SSC of each speed alone (local models) and all speeds (global model) were established using partial least squares (PLS). For global model, spectra of each sample were divided into three parts (P1, P2, and P3), three kinds of spectra partition combinations (P12, P13, and P23) were established. Results showed that S3 performed better and the influence of speed on spectra greatly affected SSC evaluation accuracy between local models. Comparatively, global model was insensitive to fruit moving speed variation and effective wavelengths (EWs) selected by competitive adaptive reweighted sampling (CARS) after Savitzky–Golay smoothing (SGS) achieved better results than local models. Importantly, 36 EWs selected by CARS after SGS of global‐P13 model achieved the best results with rp and RMSEP of 0.8419, 0.8895, 0.8948 and 0.6281, 0.5318, 0.5196°Brix, respectively. Generally, global‐P13 model with EWs is promisingly applied to online SSC prediction of apple by Vis/NIR diffuse transmission.Practical applicationsIt is of great value to study nondestructive and rapid methods for detecting apple SSC to meet the ever‐growing needs of consumers for continuous supply and high‐quality fruit. The methods and results in this study can provide basic references for Vis/NIR diffuse transmittance online detection of SSC of “Fuji” apple about fruit moving speed variation and model simplification. It is of great social value for developing a real‐time evaluation system of apple SSC in postharvest treatment or focusing on quality changing in storage shelf.

Determination of soluble solid content in multi-origin ‘Fuji’ apples by using FT-NIR spectroscopy and an origin discriminant strategy

Apple is widely planted all over the world. Origin variability influences the internal quality of apples because soil characteristics, light effects, nutrition, weather conditions, as well as growing management vary from orchard to orchard. However, if taking spectral variations caused by the origin variability of apple samples from different orchards into account, the fruit quality parameters could not be measured or predicted with high accuracy by using the current models without updates. To improve the practicability and accuracy of the prediction models, a multi-origin regression model for the determination of soluble solids content in apples from three origins by using FT-NIR spectroscopy and a model search strategy was developed in this paper. In this model, based on the wavelengths selected by competitive adaptive reweighted sampling algorithm (CARS), partial least squares discriminant analysis (PLS-DA) was trained and applied to identify the geographical origins of the apple samples. The results indicate that the samples spectra were correctly matched to the corresponding classes and a 98.1% correct classification was achieved. Partial least squares regression (PLS) was used to establish three single-origin calibration models for the determination of soluble solids content (SSC) in apples from three different origins, and meanwhile, CARS algorithm was also applied to select the most effective wavelengths for calibration models. Then, the multi-origin CARS-PLS model for determination of SSC in apples from three origins was developed combined with origin discriminant and the proposed model search strategy. It was concluded that the multi-origin CARS-PLS model achieved more satisfying results than the single-origin CARS-PLS models for the determination of SSC, with Rp and RMSEP values for the apple samples from three geographical origins being 0.921, 0.759, 0.924 and 0.661, 0.673, 0.547 °Brix, respectively. The above results indicate that it is promising to build a multi-origin CARS-PLS model to predict SSC for apples based on an origin discriminant approach to reduce the effect of geographical origin.

Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging

Near-infrared (NIR) hyperspectral imaging was used to evaluate soluble solids content (SSC) in ‘Fuji’ apples [Malus sylvestris (L.) Mill. var. domestica (Borkh. Mansf.)]. Eighty ‘Fuji’ apples were analyzed by collecting four small block samples from each one (approximately 2.0 cm × 2.0 cm × 1.5 cm). Partial least squares (PLS) regression analysis was performed to determine the relation between SSC reference data and NIR spectral data measured from each sample. The cross-validation coefficient of determination (r2) between predicted and measured SSC values is 0.89 with a root mean squared error of cross-validation (RMSECV) of 0.55%. Then, we successfully mapped SSC at a high spatial resolution (375 μm per pixel). In addition, the absorption and reduced scattering coefficients of the measured samples were determined based on a diffusion theory model. The absorption coefficients are positively correlated to the SSC values (chemical information), whereas water cored tissue content (physical information) causes a characteristic change in light scattering coefficients. The fitting results were validated by Monte Carlo simulation, and the light penetration depth in ‘Fuji’ apples was estimated to be around 0.33 cm at 1198 nm and 0.17 cm at 1450 nm, respectively.

Design of a simple non-destructive detection system using P-wave lasers for determining the soluble solids content of apples.

The simple and nondestructive detection system studied in this work uses a near-infrared (NIR) detector and parallel-polarized (P-wave) NIR lasers to determine the soluble solids content (SSC) of apples. The P-wave NIR laser in this system is incident into the apple's pulp at the Brewster angle to minimize the interference caused by interfacial reflections. After the apple has been illuminated by four P-wave NIR lasers that correspond to the specified wavelengths of the SSC chemical bonds (880, 940, 980, and 1064nm), the prediction of correlation (rp2) and the root-mean-square error for prediction (RMSEP) of the SSC are determined via partial least square regression analysis of the reflectance. Our results indicate that the use of P-wave lasers at the Brewster angle (as the angle of incidence) and the above specified wavelengths for the prediction set measurement of the SSC of apples obtained an rp2 of 0.88 and an RMSEP of 0.47°Brix. These rp2 are 6% higher, and the RMSEPs are 9% lower, than those obtained using non-polarized lasers.

Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging

A partial least squares regression (PLSR) model to map internal soluble solids content (SSC) of apples using visible/near-infrared (VNIR) hyperspectral imaging was developed. The reflectance spectra of sliced apples were extracted from hyperspectral absorbance images obtained in the 400–1000 nm range. Prediction models for SSC mapping were developed for three different measurement/sampling designs that varied in the number and size of the regions of interest (ROIs) used for apple SSC measurement and spectral averaging. Case 1 used 29 small ROIs per apple, Case II used 9 moderate-size ROIs per apple, and Case III used 5 large ROIs per apple. The optimal pre-treatment of the spectra extracted from the hyperspectral images was investigated to enhance the performance of the prediction models. The coefficients of determination and root mean square errors of the best-performing models were, respectively, 0.802 and ±0.674 °Brix for Case I, 0.871 and ±0.524 °Brix for Case II, and 0.876 and ±0.514 °Brix for Case III. The accuracy of the PLSR models was enhanced by using the spectra and SSC measured/averaged from the fewer but larger areas of the apples rather than from more numerous but smaller areas. PLS images of SSC showed the predicted internal distribution of SSC within the apples. The overall results demonstrate that hyperspectral absorbance imaging techniques may be useful for mapping internal soluble solids content of apples.

Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm.

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data

The objective of this study was to improve the detection accuracy of soluble solids content (SSC) of apples by integrating spectra and textural features. The spectral data were directly extracted from the region of interest (ROI) of hyperspectral reflectance images of apples over the region of 400–1000nm, while the textural features were obtained by a texture analysis conducted on the ROI images based on grey-level co-occurrence matrix (GLCM). A new regression method called combined partial least square (CPLS) was proposed to analyze the integrations of spectra and different kinds of textural features. In this algorithm, the score matrix matrices of the spectral data and textural features were obtained by PLS analysis separately and then used together for calibration. The prediction results indicated that the CPLS model developed with the integration of spectra and correlation feature achieved promising results and improved SSC predictions compared with the spectral data when used alone. Next, stability competitive adaptive reweighted sampling (SCARS) was conducted to select informative wavelengths for SSC prediction. The CPLS model based on the integration of SCARS selected spectra and correlation gave better results than those with the full wavelength range. The correlation coefficient and root mean square errors of prediction set and validation set were 0.9327 and 0.641%, 0.913 and 0.6656%, respectively. Hence, the integration of spectra and correlation extracted from hyperspectral reflectance images, coupled with CPLS and SCARS methods, showed a considerable potential for the determination of SSC in apples.

Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple

In this paper, the influence of variation of spectrum measurement position on the near-infrared (NIR) spectroscopy analysis of soluble solids content (SSC) of apple was studied. The spectra were collected around stem, equator and calyx positions for each apple. Partial least squares (PLS) was used to develop compensation models of SSC for each measurement position separately (local position models) and for the full data set containing all positions (global position model). The results indicated that the influence of measurement position on the spectra affected the prediction accuracy of SSC. Compared with the local position models, the global position model was well suited to control the prediction accuracy of the calibration model for SSC with respect to the variation of spectrum measurement position. Next, competitive adaptive reweighted sampling (CARS) was used for the robust global position model to select the most effective wavelengths (EWs). It indicated that the global model established with effective wavelengths (EWs-global position model) achieved more promising results, with rp and RMSEP values for three measurement positions being 0.977, 0.977, 0.955 and 0.409, 0.386, 0.486 °Brix, respectively. Moreover, the local position models based on these effective variables (EWs-local position models) were more accurate than the models built with full range spectrum. The overall results indicated that the EWs-global position model could make the variation of spectrum measurement position a negligible interference for SSC prediction.

Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’ apple

Shortwave near infrared (SWNIR) and long wave near infrared (LWNIR) spectroscopy with a novel color compensation method were compared to predict soluble solids content of apple. Linear and nonlinear regression models were considered. Eventually, independent component analysis-support vector machine (ICA-SVM) models proved to be superior to other nonlinear models. Rp was 0.9398 and RMSEP was 0.3870% for the optimal model of SWNIR, while Rp was 0.9455 and RMSEP was 0.3691% for that of LWNIR. Moreover, the results showed that color compensation could significantly improve the prediction performance of SWNIR model. Our work implies that SWNIR with color compensation has an obvious prospect in practical industrial use for real-time monitoring apple quality.

Nondestructive Measurement of Soluble Solids Content in Apples by a Portable Fruit Analyzer

Feasibility studies of using a portable fiber spectrometer in conjunction with multivariate and variable screening analysis were investigated for noninvasive inspection of the internal quality of fruit. A highly effective illumination with circular reflective accessories was designed to reflect the divergent light of the lamps onto the sample. The penetrating spectra of the apples were measured for soluble solids content (SSC) at 550 to 985 nm in transmission mode with different numbers of lamps illuminating. The illumination was optimized with six lamps by comparison to the performance of partial least square (PLS) models. Stepwise multiple linear regression (SMLR), genetic algorithm (GA), successive projective algorithm (SPA), uninformative variable elimination (UVE), and their combinations were comparatively employed to screen the important wavelengths and promote the robustness of the calibration model. Finally, the novel calibration model built using UVE-SPA-MLR method on 21 screening wavelengths (only 1 % of all) exhibited higher coefficient of prediction (r p) of 0.951 and root mean square error of prediction (RMSEP) of 0.39 % brix for the prediction set. This study shows that the accuracy of the quantitative analysis conducted by the developed analyzer can meet the requirement for practical use.

Optimization of Informative Spectral Regions in FT-NIR Spectroscopy for Measuring the Soluble Solids Content of Apple

A novel potential method, linear combination weight PLS (LCW-PLS) model, was suggested for improving the performance of routine PLS model based on selected informative regions. Moving window partial least squares (MWPLS), genetic algorithms interval partial least squares (GAiPLS) and synergy interval partial least squares (SiPLS) were used to optimize informative spectral regions from FT-NIR spectra. A total of 660 apples harvested at 2006, 2007 and 2008, were divided into calibration and prediction sets by Kennard-Stone method. The best calibration model was obtained by LCW-PLS method based on informative spectral regions of 4328–4787, 5323–5512, 5982–7135 and 7159–7463 cm− 1 selected by MWPLS procedure, and corresponding weights of 0.004, 0.070, 0.066 and 0.860, respectively. The LCW-MWPLS model was applied to predict samples, the prediction results were with RP of 0.942, RMSEP of 0.649 %Brix and RPDP of 3.10. In addition, developed LCW-MWPLS model using random two years samples was used to predict one ...

Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods

To investigate the feasibility of using dielectric spectra in nondestructively determining the soluble solids content(SSC)offruits,thedielectricconstantsandlossfactors of 160 apples of three varieties (Fuji, Red Rome, and Pink Lady) were obtained at 51 discrete frequencies from 10 to 1800 MHz with an open-ended coaxial-line probe and an impedance/material analyzer. Based on the joint x-y distances sample set partitioning (SPXY) method, 106 apples were se- lected for the calibration set and the other 54 samples were used for the prediction set. The principal component analysis (PCA), uninformative variables elimination method (UVE- PLS), based on partial least squares, and successive projection algorithm (SPA) were applied to extract characteristic vari- ables from original full dielectric spectra. The generalized re- gression neural network (GRNN), support vector machine (SVM) and extreme learning machine (ELM) modeling methods were used to establish models to predict SSC of apples, based on the original full dielectric spectra and char- acteristic variables, respectively. Results showed that four principal components were selected as characteristic variables by PCA, 15 dielectric constants and 14 loss factors at different frequencies were selected as characteristic variables by UVE- PLS, and one dielectric constant and ten loss factors were chosen as feature variables by SPA. ELM combined with SPA had the best SSC prediction performance, with calibrated correlation coefficient and predicted correlation coefficient of 0.898 and 0.908, respectively, and calibrated root-mean- square error and predicted root-mean-square error of 0.840 and 0.822, respectively. The study indicates that dielectric spectra combined with artificial neural network and chemo- metric methods might be applied in nondestructive determi- nation of SSC of apples.