Measurement Of Soluble Solids Content Research Articles

Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging

Soluble Solids Content (SSC) is an important quality attribute that represents the internal quality of fruits. Visible and near-infrared (Vis/NIR) combined with chemometric algorithms are now popular methods for non-invasive measurement and visualization of SSC. However, in fruits with a thick rind and a large flesh volume, such as watermelon, SSC is not evenly distributed across the fruit. The variability in SSC across the fruit flesh may have an adverse effect on the accuracy of quality analysis algorithms. Thus, this paper presents an accurate and efficient approach for predicting the spatial variation of SSC in watermelons using a combined framework of low-rank approximation and deep neural networks. Watermelon ‘Spania’ was selected for this study, and hyperspectral images of each watermelon were taken from various views, including the top, bottom, and two lateral views. The low-rank property is employed as a constraint in this approach to eliminate the unwanted variations in the spectral data. The low-rank component of the spectral data, free of unwanted variations, is then fed into a fully connected neural network (FNN) for the prediction of watermelon SSC values. The proposed approach obtained optimal performance in calibration and prediction with RC2=0.982, RMSEP = 0.132, and RP2=0.945, RMSEP = 0.195 respectively. Further, the prediction outcomes of the proposed method were compared with state-of-the-art such as Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), Decision Tree (DT), and Random Forest (RF). The overall results showed that combining HSI data with a low-rank and deep neural network framework is an efficient method for accurately predicting SSC in watermelons as well as correctly predicting spatial variability of SSC in individual fruits.

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R2, root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

Nondestructive determination of SSC in Korla fragrant pear using a portable near-infrared spectroscopy system

The soluble solids content (SSC) is a key parameter to affect the quality of Korla fragrant pears. Having reliable and rapid measurement of the SSC is crucial for producers and technicians. However, the equipment necessary to meet these requirements is frequently complex and expensive. This paper developed a portable nondestructive SSC detector, primarily composed of a handheld near-infrared (NIR) spectrometer in the wavelength of 900–1700 nm, a display, a lithium battery, and a Raspberry Pi board. In addition, the whole detector was only about 425 g in total. To test the performance of the prototype, we chose 100 Korla fragrant pears as our fruit samples. Different spectral pre-processing methods were combined with principal component regression (PCR) and partial least squares regression (PLSR) to obtain accurate models. To further simplify the model, the synergy interval (Si), genetic algorithm (GA), and random frog (RF) were used to choose characteristic wavelengths. Laboratory tests show that compared with PLSR and PCR based on the full-spectrum, the prediction models obtained after conducting RF selection have yielded satisfactory results. The number of wavelength features was reduced from 228 to 10, and the R2 of the model was improved from 0.942 to 0.966. The mean relative error rate in the field test was 1.41%, indicating that the developed SSC detector was also effective in the field. It can be verified that the NIR diffuse reflectance spectroscopy is a reliable tool for the nondestructive measurement of SSC in Korla fragrant pears. The appropriate pretreatment methods and selection of characteristic wavelengths can increase NIR spectroscopy accuracy in actual measurement.

Development of a low-cost modular VIS/NIR spectroscopy for predicting soluble solid content of banana

Soluble solids content (SSC) is one of the most important parameters of banana associated with taste and consumer acceptance. NIR spectroscopy has been applied for nondestructive determination of SSC, but limited studies were conducted for a low-cost and modular VIS/NIR spectroscopy. This study was conducted to develop a calibration model to predict SSC in bananas using a modular type of VIS/NIR spectroscopy in the range of 350-1000 nm by varying distances of fiber optic probe to samples. Two varieties of bananas, namely Musa acuminata × balbisiana and Musa acuminata ‘Lady Finger’ were used. Partial least square regression (PLSR) was used to build a calibration model and to predict SSC of bananas. Normalization, baseline correction, standard normal variate (SNV), and multiple scattered (MSC) correlation were used for spectra preprocessing. The research showed that using 2 cm probe-sample distance and SNV method resulted in the best model with the coefficient correlation of calibration and prediction of 0.95 and 0.87, respectively. This study proved that probe-sample distances affected the efficiency of the model for VIS/NIR spectroscopy. This work concluded that the low-cost modular VIS/NIR spectroscopy is a promising tool for SSC measurement.

Two Different Portables Visible-Near Infrared and Shortwave Infrared Region for On-Tree Measurement of Soluble Solid Content of Marian Plum Fruit

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.

Ultra-Low-Cost Self-Referencing Multispectral Detector for Non-Destructive Measurement of Fruit Quality

A very low-cost sensor is developed to non-destructively test fruits by sequentially turning on light-emitting diodes at 12 different wavelengths and measuring the reflectance in the interactance mode. The detector is tested on kiwifruit to measure soluble solids content (SSC) and dry matter (DM) non-destructively, while the performance is compared with a benchtop spectrometer. For SSC and DM measurements, a total of 378 and 200 samples of kiwifruits were measured respectively. Non-parametric regression was used for the multi-spectral detector, while a partial least squares regression model was used for the benchtop spectrometer. Different regression techniques were used as they provided the best prediction for the two different measurements. For SSC measurements, coefficient of determination (R2) of 0.83, root mean square error for prediction (RMSEP) of 0.85%, bias of −0.004%, and SDR of 2.45 were observed using the multispectral detector. The corresponding values achieved with the benchtop spectrometer were 0.98, 0.37%, 0.01%, and 5.60, respectively. For dry matter measurements with 200 kiwifruits, R2, RMSEP, bias, and SDR of 0.83, 0.61%, −0.02, and 2.44 were achieved with the prototype multispectral detector compared with the values of 0.96, 0.31%, −0.11, and 4.80, respectively, achieved with the benchtop spectrometer. The efficacy of the multispectral detector was also tested by measuring 30 navel oranges, wherein an R2 and root mean square error for calibration (RMSEC) of 0.79 and 0.47% were achieved for SSC measurements. While the performance of the multispectral detection is lower than a benchtop spectrometer, its performance is still better than low-cost spectrometers and can be used to inexpensively measure the quality of fruits in a non-destructive manner.

Potential of a double lighting imaging system for characterization of 'Hayward' kiwifruit harvest indices

Abstract The timing of harvest date for ‘Hayward’ kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson. cv. ‘Hayward’) affects its storage life and eating quality. The common method for determination of maturity (Harvest Index) is the soluble solid content (SSC), with a minimum standard of 6.2 %. Measurement of SSC, using a hand-held refractometer, is simple and cheap, but it is destructive and laborious. In this study, we explore the potential for color and fluorescence image features to provide the basis for fast, non-destructive, and portable means of determining the Harvest Index. The most prominent fruit fluorescence excitations and emissions during fruit maturation were identified using excitation-emission matrices (Ex: 280–720 nm, Em: 300–750 nm) measured with Front Face Fluorescence Spectroscopy. The most sensitive excitation-emission wavelengths (Ex: 370 nm, Em: 400–600 nm) that correlated with SSCs were selected using partial least squares regression and were used to develop a double lighting imaging system which captures color and UV-fluorescence images. A SSC prediction model was established based on color and UV-fluorescence images. The results from this system had a root mean square error of validation and correlation coefficient relationship with standard SSCs of 0.39 and 0.94, respectively. This study demonstrated that the developed double lighting imaging system has the potential to non-destructively monitor kiwifruit maturation and determine optimum harvest by using the Harvest Index.

Non-destructive measurement of soluble solids content of three melon cultivars using portable visible/near infrared spectroscopy

In this study, a non-destructive method using visible/near infrared (Vis/NIR) spectroscopy was investigated to predict the soluble solids content (SSC) of intact melons (Cucumis melo L.) cv. ‘Manao’, ‘Jinhongbao’, ‘Xizhoumi’. A set of 360 samples (120 melons of each cultivar) was used to develop the calibration model, and two location (stylar-end and equatorial locations) models were investigated independently. The samples' spectra were obtained by a portable Vis/NIR photo-diode array spectrometer operated in reflectance mode. Multiplicative scatter correction (MSC), first derivative and Savizky-Golay (SG) smoothing in turn were applied to the obtained spectra. The region from 750 to 950 nm was selected to develop NIR models combined with the partial least squares (PLS) regression method. The results indicated that the stylar-end of the intact melon was the proper location to evaluate the SSC in the intact melon due to its suitable and exclusive physiological structure. A competitive adaptive reweighted sampling (CARS) algorithm was used to select effective wavelengths. Results showed that the CARS algorithm had great potential for simplifying the variables. Furthermore, another 195 samples were used for external prediction to evaluate the CARS-PLS model's accuracy and stability, which resulted in a high determination coefficient (R2p = 0.83) and a low root mean square error (RMSEP = 0.73 oBrix).

P-Wave Visible-Shortwave-Near-Infrared (Vis-SW-NIR) Detection System for the Prediction of Soluble Solids Content and Firmness on Wax Apples.

A nondestructive system for measuring the soluble solids content (SSC) and firmness of wax apples was developed using 670, 850, 880, 940, and 980 nm visible-shortwave-near-infrared (Vis-SW-NIR) light-emitting diode (LED) light sources and a silicon (Si) photodetector. These specified wavelengths are highly correlated with the SSC and the firmness of fruit. An LED light source was incident onto the fruit as parallel-polarized waves (P-wave) at the Brewster angle (θB) to minimize the interfacial reflection and maximize the C-H and O-H bonds absorption signals from the fruit. Partial least squares (PLS) regression is used to build calibration modes and analyze the prediction of the correlation () and the root mean square error for prediction (RMSEP) of the reflected optical signals with SSC and firmness. This resulted in and RMSEP values of 0.87 and 0.66 °Bx, respectively, in SSC measurements and 0.80 and 1.16 N/cm2, respectively, in firmness measurements. Therefore, the result shows of SSC and firmness are 6.4% and 9% higher and the RMSEP are 14% and 20% lower, respectively, than those obtained using non-polarized LED light sources.

Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) Spectroscopy

With increasing living standards, more attention has been drawn to the eating quality of fruits. To meet the standards of market and consumers, on-line fruits grading in terms of the internal qualities, such as soluble solids content (SSC), is necessary in the industrial scale. Near-infrared (NIR) spectroscopy technology has advantages of being rapid, safe, non-destructive, and environmentally friendly; thus, it was widely applied in this area. Therefore, in the present work, an on-line near-infrared SSC detection system for apples based on bicone roller transportation was used to compare the influences of detection positions and double detection regions with the help of mono-branch optical fiber and binary-branch optical fiber on the performance of the prediction models. Better prediction models were obtained at the specific position than those at the random one. The system with the binary-branch optical fiber proved superior robustness, while that with the mono-branch optical fiber proved superior accuracy. The optimal model, when considering robustness and accuracy, had a root mean square error of calibration (RMSEC), determination coefficient of calibration ( $$ {R}_C^2 $$ ), root mean square error of validation (RMSEP), and determination coefficient of validation ( $$ {R}_P^2 $$ ) of 0.568%, 0.7319, 0.610%, and 0.6295, respectively. It was established by data simultaneously detected in two different regions utilizing the binary-branch optical fiber at the specific position. This work provided a possible solution on improving the model robustness in the practical application of on-line NIR detection for the SSC measurement of apples.

A novel noninvasive and cost-effective handheld detector on soluble solids content of fruits

Soluble solids content (SSC) is an important parameter for determining sweetness, maturity, and price of fruits. A fast, noninvasive, and cost-effective portable SSC detector is appreciated by fruit planters, processers, sellers, and consumers. The study developed a handheld non-destructive SSC detector which mainly consisted of a visible/near-infrared (Vis/NIR) micro-spectrometer over the wavelength of 650–1100 nm, a Raspberry Pi board, a micro-halogen lamp, a self-made “Y” shape optical fiber probe (including an incident optical fiber and a reflection optical fiber), a lithium battery, and a display. The application software developed by python language was designed to collect diffuse reflection spectra, to preprocess spectra by using Savitzky–Golay smoothing method, and to calculate the SSC of fruits based on smoothed spectra and established partial least square regression models. Kiwifruits, nectarines, and apricots, collected from several orchards, were used as fruit samples to evaluate the detection performance of the developed SSC detector. The test showed that the root-mean-squares errors of the developed SSC detector operating in Vis/NIR range were 0.9%, 0.7%, and 0.8% for kiwifruits, nectarines, and apricots, respectively. The determined SSC value could be shown on the display in 2 s after triggering SSC measurement. Moreover, the detector was just about 550 g in weight. The developed novel handheld SSC detector is a convenient and fast tool for measuring SSC of fruits nondestructively.

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.

Fruit Cluster Pruning of Tomato in an Organic High-Tunnel System

This research assessed fruit load management and production techniques for cultivating indeterminate tomatoes in a high tunnel under intensive organic management. The successful production of high-quality, high-yielding crops is important for fruit and vegetable producers, especially growers using high tunnels. High tunnels are well-suited to organic farming and can be used to grow many valuable specialty crops. Fruit load management is practiced in fruit production (e.g., apples, peaches, and grapes), but there is lack of consensus concerning the effectiveness of fruit cluster pruning on tomato (Solanum lycopersicum) and its impact on fruit yield, quality, and marketability. In addition, there is no published research on tomato cluster pruning in certified organic systems or intensively managed high tunnels (e.g., densely planted, trellised, vegetatively pruned plants) for the Front Range of Colorado. In 2016 and 2017, a randomized complete block design was used to test the effects of cluster pruning within a high tunnel on certified organic land at Colorado State University’s (CSU’s) Agricultural Research, Development, and Education Center, South. Two treatments and three tomato cultivars were selected for the study; the treatment–cultivar combinations were replicated six times within a high tunnel. The treatments involved reducing fruit loads to three fruit and six fruit per cluster, whereas plants with unpruned clusters, which naturally developed as many as 10 fruit, served as the control. Tomato cultivars evaluated were ‘Cherokee Purple’, a widely studied heirloom, and two hybrids: ‘Jet Star’ and ‘Lola’. Parameters measured included total yield, individual fresh fruit weight, soluble solids content (SSC), marketable yield, and nonmarketable yield. Individual fresh fruit weight increased for both hybrids in the three-fruit treatment, averaged over two growing seasons. ‘Cherokee Purple’ did not respond to the cluster pruning treatments. There was no decrease in total yield, across all cultivars, between treatments and the unpruned control. However, ‘Jet Star’ yielded more than the other two cultivars. In addition, SSC and marketability measurements were more influenced by cultivar than cluster pruning treatments. ‘Lola’ had a significantly greater SSC than the other two cultivars. ‘Jet Star’ had the greatest marketable yields of all cultivars tested whereas ‘Cherokee Purple’ produced greater nonmarketable (cull) yields. Cluster pruning produced larger organic tomatoes without reducing yield or quality for two of the three cultivars used in the study. Cultivar selection remains one of the greatest factors in determining yield, quality, and marketability of a crop.

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.

An online visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids content of sugarcane billets on an elevator conveyor

The aim of this research study is to propose a prototype online detection system based on the visible and near-infrared spectroscopic (vis/SW-NIR) technique for the real-time evaluation of the soluble solids content (SSC) of sugarcane billets on an elevator conveyor. The system consisted of two main parts, a cane billet elevator and a spectral acquisition device. An elevator speed of 2 m/s was used for the transfer of sugarcane billets. Spectra acquisition was performed using four 50 W tungsten halogen lamps as a light source in conjunction with vis/SW-NIR spectrometer in reflectance mode. Partial least squares regression (PLSR) was subsequently used to correlate the spectra with the experimentally determined SSC values. The model performance was then assessed using an independent prediction set. The model was found to display a coefficient of determination of prediction (R2) of 0.785, a root mean square error of prediction (RMSEP) of 0.30 and a residual predictive deviation (RPD) of 2.16. The result on the prediction set confirm that the proposed system is suitable for the online SSC measurement of the sugarcane billets on an elevator conveyor.

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.

On-tree maturity control of peach cultivars: Comparison between destructive and nondestructive harvest indices

Optimal harvest maturity is required for flavorful peach. The supply chain often errs on the side of immature fruit with subsequent consumer dissatisfaction. Maturity indices used to determine harvest date of peach include nondestructive assessments of size and background color, and destructive measurements of flesh color, firmness and soluble solids content (SSC) . More recently an index related to the absorbance of chlorophyll (IAD) can be nondestructively assessed with a DA-meter. This application is particularly useful in red-skinned cultivars in which background color changes from green to yellow are not perceptible to the naked eye. The objective and nondestructive IAD was compared with objective and destructive assessments of firmness and SSC, as indices to make harvest decisions on peach orchards. Seven peach and five nectarine cultivars [Prunus persica (L.) Batsch] were studied. Fruits were assessed with a DA-meter and the resulting IAD values were related to firmness and SSC in several consecutive samplings during the maturity period for each cultivar. Pooling all sampling dates for each cultivar, significant positive linear regressions were observed between IAD and firmness, with coefficients of determination 0.11<R2<0.65. However, in each cultivar the linear regression parameters between IAD and firmness are different and changed in sequential samplings required for maturity control and in successive harvests. No significant relationship was observed between IAD and SSC in any cultivar. In conclusion, the DA-meter cannot replace firmness and SSC measurements during on-tree maturity control, which remain essential for sensory quality management in the supply chain. .

Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear

Soluble solid content (SSC) in fruit is one of the most crucial internal quality factors, which could provide valuable information for commercial decision-making. Near-infrared (NIR) technique has effective potentials for determining the SSC since NIR was sensitive to the concentrations of organic materials. In this study, a novel NIR technique, long-wave near infrared (LWNIR) hyperspectral imaging with a spectral range of 930–2548 nm, was investigated for measuring the SSC in pear, which has never been examined in the past. A new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was proposed to select most effective variables from LWNIR hyperspectral data. The selected variables were used as the inputs of partial least square (PLS) to build calibration models for determining the SSC of ‘Ya’ pear. The results indicated that calibration model built using MC-UVE-SPA-PLS on 18 effective variables achieved the optimal performance for prediction of SSC comparing with other developed PLS models (MC-UVE-PLS and SPA-PLS) by comprehensively considering the accuracy, robustness, and complexity of models. The correlation coefficients between the predicted and actual SSC were 0.88 and 0.88 and the root mean square errors were 0.49 and 0.35 °Brix for calibration and prediction set, respectively. The overall results indicated that long-wave near infrared hyperspectral imaging incorporated to MC-UVE-SPA-PLS model could be applied as an alternative, fast, accurate, and nondestructive method for the determination of SSC in pear.

Non-invasive measurement of soluble solid content and pH in Kyoho grapes using a computer vision technique

The determination of SSC and pH in grapes could be well performed on the basis of RGB color features.

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.