Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of 'Mejhoul' and 'Boufeggous' Date Palm Fruit Using Machine Learning Models.

Date palm (Phoenix dactylifera L.) fruit samples belonging to the 'Mejhoul' and 'Boufeggous' cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the 'Mejhoul' and 'Boufeggous' cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried 'Mejhoul' reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of 'Boufeggous' were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit.

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes ((S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition.

The Estimation of Chemical Properties of Pepper Treated with Natural Fertilizers Based on Image Texture Parameters.

The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, ß-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using image analysis. Pearson's correlation coefficients, scatter plots, regression equations, and coefficients of determination were determined. For red pepper Sprinter F1, the correlation coefficient (R) reached 0.9999 for a texture from color channel B and -0.9999 for a texture from channel Y for the content of α-carotene, -0.9998 (channel a) for ß-carotene, 0.9999 (channel a) and -0.9999 (channel L) for total carotenoids, as well as 0.9998 (channel R) and -0.9998 (channel a) for total sugars. The image textures of yellow pepper Devito F1 were correlated with the content of total carotenoids and total sugars with the correlation coefficient reaching -0.9993 (channel b) and 0.9999 (channel Y), respectively. The coefficient of determination (R2) of up to 0.9999 for α-carotene content and the texture from color channel Y for pepper Sprinter F1 and 0.9998 for total sugars and the texture from color channel Y for pepper Devito F1 were found. Furthermore, very high coefficients of correlation and determination, as well as successful regression equations regardless of the cultivar were determined.

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.).

The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision-recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage.

The Changes in Bell Pepper Flesh as a Result of Lacto-Fermentation Evaluated Using Image Features and Machine Learning.

Food processing allows for maintaining the quality of perishable products and extending their shelf life. Nondestructive procedures combining image analysis and machine learning can be used to control the quality of processed foods. This study was aimed at developing an innovative approach to distinguishing fresh and lacto-fermented red bell pepper samples involving selected image textures and machine learning algorithms. Before processing, the pieces of fresh pepper and samples subjected to spontaneous lacto-fermentation were imaged using a digital camera. The texture parameters were extracted from images converted to different color channels L, a, b, R, G, B, X, Y, and Z. The textures after selection were used to build models for the classification of fresh and lacto-fermented samples using algorithms from the groups of Lazy, Functions, Trees, Bayes, Meta, and Rules. The highest average accuracy of classification reached 99% for the models developed based on sets of selected textures for color space Lab using the IBk (instance-based K-nearest learner) algorithm from the group of Lazy, color space RGB using SMO (sequential minimal optimization) from Functions, and color space XYZ and color channel X using IBk (Lazy) and SMO (Functions). The results confirmed the differences in image features of fresh and lacto-fermented red bell pepper and revealed the effectiveness of models built based on textures using machine learning algorithms for the evaluation of the changes in the pepper flesh structure caused by processing.

Review of recent applications and research progress in hybrid and combined microwave-assisted drying of food products: Quality properties.

The growing concerns over product quality have increased demand for high quality dried food products and encouraged researchers to explore and producers of such products to implement novel microwave (MW)-assisted drying methods. This paper presents a critical review of the key principles and drawbacks of MW-assisted drying as well as needs for future research. In this article, recent research into application of microwaves as an alternative heat source, applications and progress in hybrid MW-assisted drying that rely on various drying media and combined two or three stages of MW-assisted drying for the preservation of food products is reviewed critically. The effect of different MW-assisted drying methods, conditions and initial pretreatments on the thermophysical properties, color, nutritional value and rehydration potential of dried food products is discussed in detail along with the discussion on how the material properties evolve and change in structure, color, and composition during MW-assisted drying and recent attempts at mathematical modeling of these changes made for different fruits and vegetables. It should be noted that most of the published results were obtained in laboratory-scale dryers. Pilot-scale testing is needed to bridge the gap between laboratory research and industrial applications to fulfill the potential for novel hybrid and combined MW-assisted drying methods and to expand their role in food processing.

Species Composition and Trichothecene Genotype Profiling of Fusarium Field Isolates Recovered from Wheat in Poland.

Fusarium head blight (FHB) of cereals is the major head disease negatively affecting grain production worldwide. In 2016 and 2017, serious outbreaks of FHB occurred in wheat crops in Poland. In this study, we characterized the diversity of Fusaria responsible for these epidemics using TaqMan assays. From a panel of 463 field isolates collected from wheat, four Fusarium species were identified. The predominant species were F. graminearum s.s. (81%) and, to a lesser extent, F. avenaceum (15%). The emergence of the 15ADON genotype was found ranging from 83% to 87% of the total trichothecene genotypes isolated in 2016 and 2017, respectively. Our results indicate two dramatic shifts within fungal field populations in Poland. The first shift is associated with the displacement of F. culmorum by F. graminearum s.s. The second shift resulted from a loss of nivalenol genotypes. We suggest that an emerging prevalence of F. graminearum s.s. may be linked to boosted maize production, which has increased substantially over the last decade in Poland. To detect variation within Tri core clusters, we compared sequence data from randomly selected field isolates with a panel of strains from geographically diverse origins. We found that the newly emerged 15ADON genotypes do not exhibit a specific pattern of polymorphism enabling their clear differentiation from the other European strains.

Batracobdella algira Moquin-Tandon, 1846 (Hirudinida: Glossiphoniidae)--morphometric analysis and internal morphology.

There has been very few papers on leeches of Batracobdella algira. The aim of the presently reported study was to provide the measurements and, for the first time, description and characteristics of the reproductive and digestive systems of B. algira. The material used in this study was collected from frogs, Rana saharica Boulenger, 1913, in April 1987 by H. Hotz in Béja in Tunisia (11 individuals) and by R. Ben Ahmed in 2008 from the skin of Bufo mauritanicus Schlegel, 1841 in Nabeul in Tunisia (18 individuals). The relative length of the specimens ranges between 1.48 and 4.07. The digestive system of leeches has a standard structure for the species from the family Glossiphoniidae. Male and female gonopores are separated by two annuli. Ovisacs are as long as 5 neurosomits. The characteristic features of the male reproductive system of Batracobdella are sperm ducts, which are twisted in a small area and placed near to atrium. This study will hopefully contribute to a better understanding of the host-parasite relation.

Leeches (Hirudinida: Piscicolidae)--parasites of Antarctic fish from Channichthyidae family.

There has hitherto been very few research projects focusing on ectoparasites of Antarctic fishes. The presently reported study provides data on the prevalence and the intensity of leeches (Hirudinida: Piscicolidae) infecting fishes. The materials were collected in December-February 1986/87 off the Elephant Island, South Georgia, Joinville Island, and South Shetlands. The following leech taxa were recorded in the Antarctic fishes of the family Channichthyidae: Trulliobdella capitis (Brinkmann, 1947); Cryobdella antarctica Epstein, 1970; Nototheniobdella sawyeri Utevsky, 1997; and Cryobdella sp. The above findings constitute new geographic records from off Elephant and Joinville Island and South Georgia.