•A Hg (Ar) lamp was used to correct wavelength of two developed Vis/NIR devices.•Model transfer methods were compared for apple SSC detection.•PDS was recommended for PLS model transfer after ...wavelength correction.•S/B was further used to correct SSC predictions for the independent validation set.
Calibration transfer is an important step for practical applications of Visible and Near-infrared (Vis/NIR) instruments, making the developed model transferable and avoiding recalibration. A calibration transfer method between two developed portable Vis/NIR devices (master and slave devices) for predicting soluble solids content (SSC) of apples was investigated in this study. The partial least squares (PLS) calibration models based on the spectra of the master and the slave devices in the range of 550–930 nm yielded high prediction performance, with the correlation coefficient (Rp) and the root mean square error of the prediction set (RMSEP) of 0.918, 0.552 % and 0.881, 0.666 %, respectively. However, the direct use of the PLS model built by the master instrument to the slave instrument was impracticable. A Hg (Ar) lamp was used to correct the spectral dimension for the two devices, followed by the transfer performance comparison of three methods including piecewise direct standardization (PDS), spectral space transformation (SST), and calibration model transformation based on canonical correlation analysis (CTCCA). The prediction results indicated that PDS yielded better performance when the window size was 3 and the number of the transfer samples was 25, with Rp and RMSEP of 0.874 and 0.713 %, respectively. Lower spectral angle θ¯ and higher spectral correlation coefficient r¯ also illustrated that PDS had a preferable performance compared with SST and CTCCA.After PDS and slope/bias (S/B), the SSC was successfully predicted, achieving high accuracy of Rp = 0.926 and RMSEP = 0.778 %. The above results illustrated that the proposed algorithm was a promising calibration transfer method from the master device to the slave device, and could effectively compensate for the differences of spectral response between the developed Vis/NIR devices and different batches of samples.
•Soluble solids content (SSC) is an important quality attribute of fresh fruit.•The multi-cultivar model was developed to assess SSC in three cultivars of pears.•Multi-cultivar model was superiorto ...individual-cultivar model for SSC analysis.•CARS was a powerful tool to select the most effective variables.•CARS-MLR model is optimal for SSC prediction of three cultivars of pears.
Soluble solids content (SSC) is one of the most important quality attributes affecting the price of fresh fruit. The individual-cultivar model is the mostcommon SSC analysis model. However, this type of model is not the optimal for assessment of SSC in the different cultivars of fruit. In this study, the feasibility of using multi-cultivar model for quantitatively determining SSC in three cultivars of pears was observed based on visible-NIR spectroscopy. The multi-cultivar and individual-cultivar models were developed and different variable selection algorithms were used to optimize models. Results showed that the multi-cultivar model was superior to individual-cultivar models for SSC prediction of all samples and competitive adaptive reweighted sampling (CARS) did better than Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) for selection of effective variables. Based on the selected variables, CARS-PLS and CARS-MLR multi-cultivar models can achieve effective prediction for SSC of three cultivars of pears with similar detection accuracy. The coefficients of determination for prediction set (RP2) and root mean square error of prediction (RMSEP) obtained by these two types of models are 0.90–0.92 and 0.23–0.30 for three cultivars of pears. The overall results demonstrated that it was feasible to accurately determine SSC of different cultivars of pears using the multi-cultivar model, CARS was a powerful tool to select the efficient variables, and CARS-PLS and CARS-MLR were simple and excellent for the spectral calibration.
In-field and non-invasive determination of internal quality and ripeness stages allows for a selective harvest for Feicheng peach. In this study, a portable hyperspectral imager was used for on-site ...capturing the images of mid-ripe and ripe peaches on trees, and soluble solids content (SSC) and firmness of the fruit were measured as the reference standards. These samples were split into calibration set and validation set by samples set partitioning based on joint X–Y distances (SPXY) algorithm. Multiple linear regression (MLR) models were established using effective wavelengths selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), respectively. The more promising performances were achieved by RF-MLR model with Rv2 of 0.88 and RMSEV of 0.54 for SSC, and CARS-MLR model with Rv2 of 0.81 and RMSEV of 1.17 for firmness. Furthermore, LIBSVM model was employed to discriminate the ripeness of Feicheng peach using effective wavelengths selected by sequential forward selection (SFS) algorithm, with classification accuracy of 91.7% in the validation set. It can be concluded that portable hyperspectral imager can be applied to determine the internal quality and ripeness stages of Feicheng peach in orchard, providing support for the in-field and nondestructive quality inspection and timely harvesting of Feicheng peach.
•In-field capture of hyperspectral images of mid-ripe and ripe Feicheng peaches.•Establish the predictive models for soluble solids content and firmness.•Develop the discrimination model of Feicheng peaches ripeness stages.
► Vis/NIR spectroscopy was used to determine the SSC, pH and firmness of pears. ► Vis/NIR (400–1800nm) was optimal for PLS and LS-SVM models. ► The regression coefficient was an effective way for the ...selection of EWs. ► LS-SVM was superior to the linear PLS method in predicting SSC, pH and firmness. ► EW-LS-SVM could be very helpful for real-time monitoring of the quality of pears.
Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the soluble solids content (SSC), pH and firmness of different varieties of pears. Two-hundred forty samples (80 for each variety) were selected as sample set. Two-hundred ten pear samples (70 for each variety) were selected randomly for the calibration set, and the remaining 30 samples (10 for each variety) for the validation set. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different spectral preprocessing techniques were implemented for calibration models. Different wavelength regions including Vis, NIR and Vis/NIR were compared. It indicated that Vis/NIR (400–1800nm) was optimal for PLS and LS-SVM models. Then, LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS models. Next, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and a good prediction precision and stability was achieved compared with PLS and LV-LS-SVM models. The correlation coefficient of prediction (rp), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.9164, 0.2506 and −0.0476 for SSC, 0.8809, 0.0579 and −0.0025 for pH, whereas 0.8912, 0.6247 and −0.2713 for firmness, respectively. The overall results indicated that the regression coefficient was an effective way for the selection of effective wavelengths. LS-SVM was superior to the conventional linear PLS method in predicting SSC, pH and firmness in pears. Therefore, non-linear models may be a better alternative to monitor internal quality of fruits. And the EW-LS-SVM could be very helpful for development of portable instrument or real-time monitoring of the quality of pears.
•Crucial features of dried persimmon fruits are required to monitor during process.•Moisture, water-soluble tannin and soluble solid contents was evaluated.•Multiple models were developed based on ...hyperspectral images.•LS-SVR and PLSR models showed the higher correlation coefficients.•Successive projection algorithm (SPA) is an efficient method to simplify models.
The crucial features of persimmon are required to detect real-time moisture, water-soluble tannin, and soluble solids contents during the drying process. This study developed a method based on hyperspectral imaging (HSI) to execute online and non-destructive assaying of persimmon features. A total of 144 samples were collected, and 150 bands were scanned. The spectral data were analyzed by partial least squares regression (PLSR), principal component regression (PCR), least squares support vector regression (LS-SVR), and radial basis function neural network (RBFNN) with seven preprocessing methods. LS-SVR provided excellent performance for moisture content prediction, while PLSR was better in the analysis of water-soluble tannin and soluble solids contents. Successive projection algorithm (SPA) was used to select the optimal wavelengths to simplify the models, and about twenty important variables were chosen. Overall, these results indicate that HSI could be considered a valuable technique to quantify chemical constituents in dried persimmon fruits.
► Blueberry’s SSC and firmness were predicted using hyperspectral reflectance images. ► Images were acquired in an in-house built pushbroom hyperspectral imaging system. ► Better predictions for ...firmness (R=0.87) than for SSC (R=0.79) were achieved. ► Fruit orientation had no or little effect on the prediction of firmness and SSC. ► Results show this technique is promising for assess internal quality of blueberries.
Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packing houses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500–1000nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R=0.87) were obtained, compared to SSC predictions (R=0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance the product quality and marketability.
•Soluble Solids Content prediction was accomplished with hyperspectral imaging.•The wavelength selection method combined clustering and Margin Influence Analysis performs well.•Support Vector ...Regression models are established based on characteristic bands.
As an effective non-destructive detection technology, hyperspectral imaging (HSI) is widely applied in evaluating the quality of fruit, such as soluble solids content (SSC) in apples, oranges, pears, and sugar content in grape berries, and internal bruising in blueberries. But due to the redundancy in hyperspectral data, the prediction performance significantly relies on the characteristic wavelength selection. Most previously published studies rarely simultaneously consider the correlations among different spectral bands and the extraction of characteristic bands from the original spectrum. To solve the problem, this study explores the application of hyperspectral technology to determine soluble solids content (SSC) in Korla pears. It focuses on reducing the hyperspectral data by applying a new effective wavelength selection method called Group sampling Margin Influence Analysis (GsMIA). It combines band-correlation and band-influence to enhance the selection of key bands. GsMIA contains three steps: grouping, margin influence analysis, and second selection. Combined with S-G smooth first derivative preprocessing method and support vector regression models, the proposed method can effectively yield good prediction performance with only 7.81 % of the original wavelengths, and the experimental results of comparison with several state-of-the-art methods on Korla fragrant pears SSC datasets further demonstrate the effectiveness and superiority of the proposed method.
Similar to apple, pear is one of the most important horticultural crops with high nutritional and economical value. The biological and economic traits of the specie, as well as the fruits quality, ...make the pear growing much appreciated and to be given an increasing importance. Five pear cultivars ('Paramis', 'Paradox', 'Paradise', 'Isadora', 'Pandora') registered in the last period by Research Institute for Fruit Growing Pitesti, Romania and one selection 'SP06C2P5' ('Packham’s Triumph' x 'Monica') were investigated regarding their physical-chemical parameters. Fruits quality attributes were determined by the external (weight, size, skin lightness) and internal (firmness, total soluble solids, acidity, pH). Observations and determinations were made in the season of 2019, 2020 and 2021, in field trial planted in 2016. For this study, comparisons were made with 'Monica', the most spread bred Romanian cultivar. Significant differences were encountered among the different cultivars for most of the quantitative characters, such as fruit weight, caliber, lightness, total soluble solids (TSS) and acidity. Based on these results, ‘Pandora’ cv. had the highest annual average of weight (243.17g), ‘Isadora’ cv. the highest amount of TSS (14.63%), ‘SP06C2P5’ the highest amount of malic (0.74%), citric (0.71%) and tartric acid (0.80%). The data referring to the external and internal fruit quality traits of new bred Romanian pear cultivars are useful for growers, but also to enrich germplasm collection and to select proper parents for breeding.
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