Laser-induced backscattering imaging for classification of seeded and seedless watermelons

This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658 nm using a backscattering imaging system developed for the purpose. The pre-processed datasets extracted from the backscattering images were analysed using principal component analysis (PCA). The datasets were separated into training (75%) and testing (25%) datasets as the inputs in the classification algorithms. Three multivariate pattern recognition algorithms were used including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). The QDA-based algorithms obtained the highest overall average classification accuracies (100%) for both the seeded and seedless watermelons. The LDA and kNN-based algorithms also obtained quite high classification accuracies with all the accuracies above 90%. The laser-induced backscattering imaging technique is potentially useful for classification of seeded and seedless watermelons

Laser backscattering imaging as a control technique for fluid foods: Application to vegetable-based creams processing

[EN] In this work, the application of a laser backscattering image technique as a non-destructive quality control technique for fluid food matrices was studied. The used food matrices were vegetable-based creams, which were modified according to the combination of four production factors (raw material, biopolymer type, biopolymer concentration and homogenisation system) in order to obtain a wide space of variance in terms of physico-chemical properties (52 different creams). All the creams were characterised based on that imaging technique using pre-designed descriptors extracted from the captures of the generated laser patterns. The capacity to characterise creams presented by the imaging and physico-chemical data (rheology and syneresis) was compared, and the effect of each production factor on their captured variance was evaluated. Both characterisations were similar. This parallelism was proved by modelling the relationship between them by carrying out regression studies. The regression coefficients were successful for most physico-chemical variables. However, the prediction of creams¿ properties was maximised when done over the linear combination of them all. Thus the imaging descriptors collected enough variance from the cream categories to place them according to their physico-chemical properties into the generated space of physico-chemical variance. The results allowed us to conclude that this technique can be applied for the non-destructive quality control of fluid-food matrices for production processes with a wide spectrum of product categories.Verdú Amat, S.; Pérez Jiménez, AJ.; Barat Baviera, JM.; Grau Meló, R. (2019). Laser backscattering imaging as a control technique for fluid foods: Application to vegetable-based creams processing. Journal of Food Engineering. 241:58-66. https://doi.org/10.1016/j.jfoodeng.2018.08.003S586624

Characterization of optical parameters in seeded and seedless watermelon using laser-induced backscattering imaging

Monitoring of watermelon fruit quality is essential in order to regulate proper postharvest handling and yield production. Problems arise in forecasting the quality parameters of watermelon during storage as the shelf-life of the fruit is only lasts for three weeks. The non-climacteric nature of the fruit is also related to high perishability which does not undergo a continuous process to ripen after being harvested. In the present study, laser-induced backscattering imaging was used to determine the firmness, soluble solids content (SSC), pH, moisture content (MC), and colour changes of watermelons in seven interval days starting from storage day 0 after harvesting (day 0, day 4, day 8, day 12, day 15, day 18, and day 21). Two types of watermelon cultivars were used in this study; seeded watermelon (Black Beauty) and seedless watermelon (Red Seedless). The backscattered images of the fruit surface were obtained at six different locations using laser diodes emitting at 658 nm wavelength. The backscattered images were analysed and the feature information was extracted based on the backscattering image parameters which are minor length, major length, perimeter, maximum intensity, minimum intensity, and mean intensity. The standard reference methods were carried out after the image acquisition process in order to determine the quality parameter measurements. The multivariate analysis was used to optimise the classification between seeded and seedless watermelons, as well as the regression models between backscattering data and quality parameters, were also discussed. Principal component analysis (PCA) was used to classify the seeded and seedless watermelons into two different classes according to the physicochemical changes. Partial least squares (PLS) regression with full cross-validation method was used to establish the regression models between the backscattering data and quality parameters. The firmness values obtained were 5.07 to 3.24 kg/cm2 and 4.78 to 3.03 kg/cm2, whereas the SSC values achieved 9.06 to 5.66 Brix and 8.52 to 6.41 Brix for the seeded and seedless watermelons, respectively. The pH values were 5.27 to 7.10 pH and 5.60 to 6.42 pH while the moisture content values revealed 95.46 to 82.79 % w.b. and 94.53 to 86.43 % w.b. for the seeded and seedless watermelons, respectively. For colour parameters, the L* values ranged from 24.08 to 51.06, whereas the b* values ranged from 11.52 to 36.84. The chroma and hue values were also increased from 12.41 to 38.95 and 104.55º to 111.60º, respectively. The a* value reduced significantly (P<0.05) from -4.32 to -13.06 for both seeded and seedless watermelons. For seedless watermelon, the colour prediction (L*, a*, b*, chroma, and hue) gave the highest coefficient of determination (R2) with all of them above 0.90. Meanwhile, the firmness prediction gave the highest R2 of 0.92 for seeded watermelon. On the whole, it is concluded that the application of laser-induced backscattering imaging is a promising technique for assessing quality parameters of watermelons during storage. The proposed approach has significant potential as a well-controlled system for developing automated sorting and grading system in the future