Apple Soluble Solids Content Research Articles

Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying

Continuous monitoring of food properties during drying is of stark relevance for the food processing industry. The main task of this research is to apply laser backscattering technology to simultaneously predict variations in hardness, moisture content and soluble solids content (SSC) of apples during drying. A laser diode with a wavelength of 635 nm was used to non-destructively analyze photon migration in the apple tissue. The backscattering area in pixel numbers, representing the illuminated area after laser light injection, and light luminescence measured by grey values were used for estimating changes in internal quality parameters during drying. A decrease in moisture content caused a reduction in photon scattering determined by diminished light propagation in the fruit tissue. Additionally, scattering images showed less photon migration as the SSC increased due to the light absorption of light at 635 nm by fruit compounds. To estimate moisture content, calibration models gave the highest coefficients of determination ( R 2 = 0.8 and 0.89) with standard errors of cross validation (SECV) of 11.6 and 9.8 for backscattering area and light luminescence, respectively. However, predictions obtained for SSC were slightly lower. Laser light measurement at 635 nm was found to be adequate for predicting changes in moisture content and SSC of apple during drying over different stages. On the contrary, photon scattering at 635 nm is not recommended as estimator of change in hardness during apple drying, based on the results.

Optimal Wavelength Selection for Hyperspectral Scattering Prediction of Apple Firmness and Soluble Solids Content

Hyperspectral scattering is a promising technique for nondestructive quality measurement of apple fruit, and extraction of the most useful information from the hyperspectral scattering data is critical for accurate assessment of fruit firmness and soluble solids content (SSC). In this article, a hierarchical evolutionary algorithm (HEA) approach coupled with subspace decomposition and partial least squares regression is proposed to select the optimal wavelengths from hyperspectral scattering profiles of 'Golden Delicious' apples for predicting fruit firmness and SSC. Six hundred apples were tested in the experiment, 400 of which were used for calibration and the remaining 200 apples for validation. Seventeen optimal wavelengths were selected for firmness prediction, which nearly spanned the entire spectral range of 500 to 1000 nm, and 16 optimal wavelengths, all of which were above 600 nm, were selected in the SSC prediction model. The model using the 17 optimal wavelengths for predicting firmness yielded better results (r = 0.857, root mean square error of prediction or RMSEP = 6.2 N) than the full spectrum model (r = 0.848, RMSEP = 6.4 N). For predicting SSC, the model using the 16 optimal wavelengths also yielded better results (r = 0.822, RMSEP = 0.78%) than the full spectrum model (r = 0.802, RMSEP = 0.83%). The HEA approach provided an effective means for optimal wavelength selection and improved the prediction of firmness and SSC in apples compared with the approach using the full spectrum.

The performance of ν-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy

The ν-support vector regression ( ν-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of ν-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). The influence of SVR parameters on the predictive ability of model was investigated. The results indicated that the parameter ν had a rather wide optimal area (between 0.35 and 1 for the apple data). Therefore, we could determine the value of ν beforehand and focus on the selection of other SVR parameters. For analyzing SSC of apple, ν-SVR was superior to PLSR and BP-ANN, especially in the case of fewer samples and treating the noise polluted spectra. Proper spectra pretreatment methods, such as scaling, mean center, standard normal variate (SNV) and the wavelength selection methods (stepwise multiple linear regression and genetic algorithm with PLS as its objective function), could improve the quality of ν-SVR model greatly.

Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple

A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.

Predicting soluble solid content and firmness in apple fruit by means of laser light backscattering image analysis

Laser-induced light backscattering imaging was studied regarding its potential for analyzing apple soluble solids content (SSC) and fruit flesh firmness. Images of the backscattering of light on the fruit surface were obtained from ‘Elstar’ and ‘Pinova’ apples using laser diodes emitting at five wavelength bands. Image processing algorithms were tested to correct for dissimilar equator and shape of fruit. Particularly the frequencies of gray scale intensities obtained for selected wavelengths were used for the first time to calibrate on the fruit firmness and SSC using partial least squares regression. Calibration with highest performance for predicting ‘Elstar’ SSC was based on the corrected intensity frequency of raw data set with correlation coefficient of r = 0.89 and standard error of cross validation %SECV = 4.14. For evaluating ‘Elstar’ flesh firmness, corrected frequency gave the highest r = 0.90, and %SECV = 5.49. An inter-cultivar test-set validation of the method resulted in SEP < 13% for SSC and firmness prediction.

Multispectral imaging for predicting firmness and soluble solids content of apple fruit

Firmness and soluble solids content (SSC) are important quality attributes for apples and many other fresh fruits. This research investigated the feasibility of using multispectral imaging to quantify light backscattering profiles from apple fruit for predicting firmness and SSC. Spectral images of the backscattering of light at the fruit surface, which were generated from a focused broadband beam, were obtained from Red Delicious apples for five selected spectral bands (10 nm bandpass) between 680 and 1060 nm. Ratios of scattering profiles for different spectral bands were used as inputs to a backpropagation neural network with one hidden layer to predict fruit firmness and SSC. The three ratio combinations with four wavelengths (680, 880, 905, and 940 nm) gave the best predictions of fruit firmness, with r=0.87 and the standard error of prediction (SEP)=5.8 N. Only two ratios with three wavelengths of 880, 905 and 940 nm were needed for predicting the SSC of apples with r=0.77 and SEP=0.78%. The multispectral imaging technique is promising for predicting firmness and sweetness of apples.

Effect of biological variability on the robustness of NIR models for soluble solids content of apples

A statistical analysis was performed on a large spectral data set to analyse the effect of orchard, season and cultivar. Season and cultivar were responsible for a major amount of the spectral variability, whereas the influence of the orchard was low and only appeared for certain cultivars during specific seasons. The robustness of the calibration models for soluble solids content with respect to the three factors was tested based on external validations. It was found that the accuracy of the models increased considerably when including more variability in the calibration set. Further, overfitting of the calibration model was avoided. On the other hand, adding more data to the calibration set increased the chance of adding atypical data, which resulted in reduced model accuracy. It is, therefore, important to construct the calibration data set in such a way that it is representative for future measurements. When the effect of a certain factor is known a priori, e.g. cultivar, it is recommended to use specific calibration models.