Evaluation Of Soluble Solids Content Research Articles

Non-destructive evaluation of soluble solids content in navel orange by an on-line visible near-infrared system with four parallel spectrometers

Non-destructive evaluation of soluble solids content in navel orange by an on-line visible near-infrared system with four parallel spectrometers

Rapid and nondestructive evaluation of soluble solids content (SSC) and firmness in apple using Vis–NIR spatially resolved spectroscopy

Abstract Visible–near infrared (Vis–NIR) spectroscopy is a rapid and nondestructive method used to characterize organic compounds in postharvest fruit and vegetable assessment. However, developing robust calibration models is a challenge as conventional spectrometers collect only the cumulative effects of light absorption and scattering. In this study, a multifiber-based Vis–NIR spatially resolved spectra measurement system was designed for simultaneous evaluation of soluble solid content (SSC) and firmness in apple. Thirty silica fibers separated into five groups at 1, 2, 3, 4, and 5 mm away from the light illumination point and connected to a cost-effective Vis–NIR hyperspectral imaging camera were used to acquire spectral data with an improved signal-to-noise ratio (S/N) by a two-step signal averaging process (i.e., 30 camera pixels per fiber and six optical fibers per group). Reflectance ratio spectra were then calculated by dividing the diffusely reflected light intensity detected at distance d +△ by that detected at distance d to realize a light reference-free approach. Finally, the useful explanatory variables were selected by competitive adaptive reweighted sampling (CARS) to construct individual calibration models for various regions. The coefficients of determination ( R c a l 2 ) and the root mean square errors (RMSEcal) of the best-performing calibration models were approximately 0.97 and 0.20 % for SSC and 0.96 and 0.37 N for firmness, respectively. Furthermore, the predicted results were 0.92 and 0.35 % for SSC and 0.87 and 0.71 N for firmness. Our method offers low-cost and portable detection of SSC and firmness for postharvest fruit evaluation.

Nondestructive evaluation of soluble solids content in tomato with different stage by using Vis/NIR technology and multivariate algorithms

In this study Vis/NIR spectroscopy was applied to evaluate soluble solids content (SSC) of tomato. A total of 168 tomato samples with five different maturity stages, were measured by two developed systems with the wavelength ranges of 500–930 nm and 900–1400 nm, respectively. The raw spectral data were pre-processed by first derivative and standard normal variate (SNV), respectively, and then the effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and random frog (RF). Partial least squares (PLS) and least square-support vector machines (LS-SVM) were employed to build the prediction models to evaluate SSC in tomatoes. The prediction results revealed that the best performance was obtained using the PLS model with the optimal wavelengths selected by CARS in the range of 900–1400 nm (Rp = 0.820 and RMSEP = 0.207 °Brix). Meanwhile, this best model yielded desirable results with Rp and RMSEP of 0.830 and 0.316 °Brix, respectively, in 60 samples of the independent set. The method proposed from this study can provide an effective and quick way to predict SSC in tomato.

Non-destructive evaluation of soluble solids content of apples using a developed portable Vis/NIR device

A portable visible and near-infrared (Vis/NIR) device could evaluate and monitor internal qualities of fruit on-tree, as well as during storage conditions after harvest. A portable Vis/NIR device which consisted of a commercial spectrometer in the spectral range of 400–1000 nm, an interactance fibre optic probe, a novel switch system, and a microcontroller, was developed and its ability for apple soluble solids content (SSC) prediction was evaluated. A switch system was designed for spectra collection, resulting in the acquisition of three spectra for each measurement of apple fruit, namely the white reference, dark reference, and sample spectrum, which can be used to correct the spectrum of apple fruit dynamically. The results showed that the dynamic correction was more promising than the static correction in which the reference spectra were obtained only once. A model for SSC was built using partial least square (PLS), with the coefficient of determination of prediction ( R p 2 ), the root mean square error of prediction (RMSEP), and the ratio of the standard deviation of the reference destructive SSC to the RMSEP (RPD) of 0.777, 0.561%, and 2.114, respectively. The model was then embedded in the custom software to make it possible for the portable device to predict SSC of apple directly, followed by validation using independent sets. The validation results gave R p 2 , RMSEP, and RPD of 0.764, 0.672%, 2.029, respectively for SSC prediction under laboratory conditions, and 0.684, 2.777%, and 0.381, respectively for apples on-tree. The prediction results in the field were improved dramatically using the model built by the field data, with R p 2 , RMSEP and RPD of 0.690, 0.604%, and 1.794, respectively. The overall results showed that the developed device had considerable potential to detect the SSC of apple in practical situations.

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.

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.

Comparative analysis of models for robust and accurate evaluation of soluble solids content in ‘Pinggu’ peaches by hyperspectral imaging

Soluble solids content (SSC) is one of the most important factors determining the quality and price of fresh fruits. This study was carried out to compare the different models for more robust and accurate evaluation of SSC in ‘Pinggu’ peaches using hyperspectral imaging in the visible and near infrared spectral range (400–1000 nm). The local region models and multi-region combination model based on full wavelengths were established and compared by partial least squares, respectively. The results of model analysis showed that multi-region combination model was superior to local region models due to insensitivity to the variation of the local regions of interest. Then, four typical wavelength selection algorithms including Monte Carlo cross-validation, successive projections algorithm, competitive adaptive reweighted sampling and random frog were utilized to select the effective wavelengths (EWs) for rapid quantitative determination of SSC, respectively. It was found that random frog was the most effective algorithm for local region models and multi-region combination model in SSC analyses. Considering the practical industrial application, finally, different type of models developed based on EWs selected by random frog were applied to predict the SSC of whole ‘Pinggu’ peaches from new test samples. EW-multi-region combination model achieved the optimal prediction results with rp of 0.86 and RMSEP of 0.67. The overall results of this study revealed that the hyperspectral imaging coupled with effective wavelength selection can be used to non-invasively and fast measure the SSC of ‘Pinggu’ peaches and a more robust and accurate model could be established based on multi-region information rather than any local region. These results can provide a useful reference for global evaluation of the internal quality attributes of fresh fruits.

Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection.

Experimental results are presented on the use of partial least squares (PLS) regression and wavelength selection for the definition of models for visible-near-infrared (Vis-NIR) evaluation of soluble solids content in fruits. First, the relatively easy to deal with-but still not studied in the literature-case of cherry fruit is presented in detail. By using a very simple selection scheme, involving the subsampling of the spectral interval from 600 to 1100 nm with a fixed step, accurate models were found, consistently showing very favorable combinations of SEC and SEP values, in the 0. 50 degrees Brix range for a total variation of about 15 degrees Brix. Apricot fruit represented a more difficult species, and wavelengths to be included in the calibration had to be individually selected for the best results. Nevertheless, parsimonious models could be found, including a total of 38 spectral lines and leading to SEP values at the 0.75 degrees Brix level.