The effect of hawthorn berries ripeness on the physicochemical, structural and functional properties of hawthorn pectin (HP) and its potential in sweet cherry preservation were investigated. With the ...advanced ripeness of hawthorn berries, the galacturonic acid (GalA) content decreased from 59.70 mol% to 52.16 mol%, the molecular weight (Mw) reduced from 368.6 kDa to 284.3 kDa, the microstructure exhibited variable appearance from thick lamella towards porous cross-linked fragment, emulsifying activity and emulsions stability, antioxidant activities, α-amylase and pancreatic lipid inhibitory capacities significantly increased. The heated emulsion stored for 30 d presented higher creaming index and more ordered oil droplets compared to the unheated emulsion. With the extended berries ripeness, the firmness of HP gels remarkably decreased from 225.69 g to 73.39 g, while the springiness increased from 0.78 to 1.16, HP exhibited a superior inhibitory effect in water loss, browning, softening, and bacterial infection in sweet cherries preservation.
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•GalA, Mw and particle size of HPs decreased with the advanced berries ripeness.•Adsorption and emulsification of HPs enhanced with the increasing berries ripeness.•Berry ripeness extension improved the antioxidation and lipase inhibition of HPs.•Increased ripeness promoted springiness while reduced firmness of HPs gels.•Higher ripeness brought superior effect of HPs coating on cherry preservation.
Agriculture sector plays a key role in the economic development of India. The task of fruit grading is vital in the agricultural industry because there is a great demand for high quality fruits in ...the market. However, fruit grading by human is inefficient, labor intensive and prone to error. The automated grading system not only speeds up the time of processing, but also minimizes error. There is a great demand for tomatoes in both local and foreign markets. The tomato fruit is very delicate and hence careful handling of this fruit is required during grading. Thus, this paper proposes an automatic and effective tomato fruit grading system based on computer vision techniques. The proposed quality evaluation method consists of two phases: development of hardware and software. The hardware is developed to capture the image of the tomato and move the fruit to the appropriate bins without manual intervention. The software is developed using image processing techniques to analyze the fruit for defects and ripeness. Experiments were carried out on several images of the tomato fruit. It was observed that the proposed method was successful with 96.47% accuracy in evaluating the quality of the tomato.
In the face of the COVID-19 pandemic and the global sales trend of fruits, automatic ripeness grading of fruits is of great significance for enterprises, reducing labor-related costs and precise ...resource regulation of the fruit industry chain. The wrong ripeness grading may lead to inferior products entering the market chain and resulting in over ripeness, spoilage, quality degradation, and economic loss issues. Traditional manual grading and machine vision-enabled grading methods are facing a series of challenges, such as low efficiency, inconsistent grading standards, and vulnerability to environmental interferences. Therefore, a flexible sensing enabled intelligent manipulator system (FSIMS) is developed for efficient, automatic and accurate ripeness grading of avocados, one of the most popular and economically valuable fruits in the world. When avocados of different ripeness level are gripping, the flexible sensing units attached in the clamps can sense the firmness of the contact points and feedback different pressure values of the system, which could accurately determine four ripeness levels of avocados. Compared with traditional manual or machine vision enabled fruit ripeness grading methods, the designed FSIMS could achieve better grading effect (97.5% accuracy), and the system also has a faster grading speed (the fast-grading speed could reach 1.3 s/time) and high environmental robustness. The application of FSIMS could effectively reduce the waste of avocados in the market supply chain, greatly alleviating the labor-intensive and inefficiency problems of the fruits ripeness grading, thereby promoting the more sustainable and cleaner production of the avocado industry.
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•Reflectance and fluorescence spectra of citrus cv Batu 55 has been collected.•Ripeness related i.e. brix to acid ratio has been obtained.•Prediction model has been developed and ...compared using different features set.•Prediction model based on combination spectra show higher accuracy.
Optical characteristics of Mandarin Orange cv. Batu 55 of different maturity level has been obtained using reflectance (Vis-NIR) and fluorescence spectroscopy. Spectra features of both reflectance and fluorescence spectroscopy have been evaluated to develop a ripeness prediction model. Spectra dataset and reference measurements were subject to the partial least square regression (PLSR) analysis. The best prediction models were using reflectance spectroscopy data showing the coefficient of determination R2 up to 0.89 and root mean square error (RMSE) of 2.71. On the other hand, it was found that fluorescence spectroscopy showed interesting spectra change in correlation with the accumulation of bluish and reddish fluorescence compounds in the lenticel spots on the fruit surface. The best prediction model using fluorescence spectroscopy data showed the R2 of 0.88 and RMSE of 2.81. Besides that, it wa found that combining spectra of reflectance and fluorescence features could increase the R2 of the partial least square regression (PLSR) model with Savitzky-Golay smoothing, up to 0.91 for brix-acid ratio prediction with RMSE 2.46. These results show the potential of the combined reflectance-fluorescence spectroscopy system for Mandarin ripeness assessment.
Areca nut (Areca catechu) is a kind of palm plant that grows in Asia and Africa, the eastern part of the Pacific and in Indonesia itself, areca nut can also be found on the islands of Java, Sumatra ...and Kalimantan. At the stage of classifying the maturity of the betel nut so far, it is still using the manual method which at that stage has subjective weaknesses. Based on these problems, researchers will create a system that is able to classify the level of maturity of areca nut using HSV feature extraction with assistance at the classification stage using the KNN method. In this study, 842 datasets were used which were divided into 3 types of classes, namely ripe, unripe and old fruit. The dataset was divided into 683 training data and 159 test data. In the next stage, the data is tested using the K-Nearest Neighbor method by calculating the closest distance using k = 1. From the results of the calculation of the closest distance k1 produces an accuracy rate of 87.42%.
Kata kunci— Matlab, Areca Ripeness, KNN, HSV.
•Portable NIR spectrometer was used for on-line prediction of carambola maturity stages.•Performance of NIR spectrometer to predict physicochemical properties was tested.•PLSR model for moisture ...content prediction showed a RER > 10 and RPD > 2.•PLS-DA achieved 81.3% for correct classification of carambola according maturity.•GA and iPLS improved regression models performance with reduction of RMSEP.
Carambola is a tropical fruit with rising value in developed countries due to its nutritional value and exotic aspect. It is important to assess carambola quality in different maturity stages to estimate a “fair price” and to assign fruit for specific applications and markets. This work reported the use of a portable NIR spectrometer in the range of 900 to 1700 nm as a non-destructive, chemical-free technique for determination of carambola physicochemical properties, according to maturity stage. Colour, total soluble solids, ascorbic acid, moisture, pH and titratable acidity analysis were performed for 177 fruit from two clones and four maturity stages (MS1, MS2, MS3 and MS4). PLS-DA and PLSR models were built to classify carambola according to maturity stage and to estimate its physicochemical properties, respectively. Several pre-processing were tested and among them the new algorithm introduced in the (SNV) pre-processing, the variable sorting for normalization (VSN), allows the improvement of the signal shape and the model interpretation. Genetic algorithm (GA) and interval partial least square (iPLS) were tested for improving model performance. The PLS-DA model based on important variables selected by iPLS achieved the best performance with 84.2% accuracy to classify carambola according to maturity stage. Variable selection (iPLS and GA-PLS) allowed an improvement in the performance of the PLSR models, with pH and moisture content achieving (R2P of 0.78 and 0.74), (RMSEP of 0.2 and 0.87), (RPD of 2.01 and 2.23) and (RER of 8.02 and 10.38), respectively, which is acceptable for screening. Portable NIR spectrometer, which can be considered low-cost when compared to benchtop spectrometers, in tandem with chemometrics can be a promising tool to assess the composition and to classify carambola according to maturity stage.
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Sorting system for tomato is one of the important things to deploy to achieve better quality of tomato. Nowadays, many sorting system is done manually and this could spend a lot of time and become ...inefficient. One method can be implemented in the sorting system by using Convolutional Neural Network (CNN) method to classify the ripeness of tomatoes. The objective of this research is to classify the ripeness of tomatoes based on the color of tomatoes. There are three categories of color level such as green for raw tomato, turning for half-ripe tomato and red for ripe tomato. Research methodology of this research is data collection, data pre-processing and image maintenance, CNN model, and training data. The image used in this research are 1148 images. These images were taken manually using smartphone camera in outdoor environment. These images were used to build CNN model. The results of this research show that by testing 10 images of tomatoes achieved raw tomatoes close to 90%, ripe tomatoes close to 90% and half-ripe tomatoes close to 80%. Based on the results, CNN can be used as a good alternative in image classification tasks.
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•A new index for evaluating the ripening of apples is proposed: VRPI.•The VRPI is an integration of several apple parameters and correlates with other ripeness indices.•Fusing and ...correcting models can improve the performance of VRPI prediction under seasonal variations.•This index shows promise for application to other fruits.
Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.
•Wavelet transform was used as pre-processing tool in Vis/NIR spectral analysis.•Developed models showed better regression performance than previous studies.•Wavelet transform showed acceptable ...performance for denoising of spectral data.
Quality assessment of fruits and vegetables is important for all involved in production, processing, trading and even consumption of the products. This study was conducted for non-destructive estimation of moisture content (MC), soluble solids content (SSC), pH and firmness of Gala apple samples using near-infrared spectroscopy in the 350–2500 nm range by application of wavelet transform for pre-processing of raw spectral data. Wavelet transform was combined with other usual pre-processing functions widely used for constructing PLS regression models. The models' predictive capability was evaluated using correlation coefficient of determination (R2), root mean square error of estimation (RMSE) and correlation coefficient (R). The best calibration and validation results were obtained for MC with 8 factors, R2C = 0.90, RMSEC = 0.0042, R2CV = 0.88, and RMSECV = 0.0047. The PLS regression parameters for SSC with 9 model factors, were R2C = 0.90, RMSEC = 0.37, R2CV = 0.86, and RMSECV = 0.45. The calibration and validation model parameters for pH and firmness with 10 model factors were R2C = 0.87, RMSEC = 0.04, R2CV = 0.84, RMSECV = 0.05, and R2C = 0.76, RMSEC = 2.23, R2CV = 0.0.59, and RMSECV = 3.04, respectively. In the prediction set, the model that was developed for moisture content had RMSEP = 0.009, R2p = 0.6. The R2p, and RMSEP for SSC were, 0.87 and 0.55, respectively. The model prediction parameters for pH were 0.72 and 0.06 for, R2p and RMSEP, respectively. Finally, predicted firmness of apple samples were acceptable (R2p = 0.65 and RMSEP = 3.86). Because of noise removal ability, application of wavelet transform for pre-processing on spectral data led to accurate, simple and fast development of PLS regression models.
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