Online measuring of quality changes of banana slabs during convective drying

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

Online Machine Vision-Based Modeling during Cantaloupe Microwave Drying Utilizing Extreme Learning Machine and Artificial Neural Network

Online microwave drying process monitoring has been challenging due to the incompatibility of metal components with microwaves. This paper developed a microwave drying system based on online machine vision, which realized real-time extraction and measurement of images, weight, and temperature. An image-processing algorithm was developed to capture material shrinkage characteristics in real time. Constant-temperature microwave drying experiments were conducted, and the artificial neural network (ANN) and extreme learning machine (ELM) were utilized to model and predict the moisture content of materials during the drying process based on the degree of material shrinkage. The results demonstrated that the system and algorithm operated effectively, and ELM provided superior predictive performance and learning efficiency compared to ANN.

Modeling of Drying Kinetics of Banana (Musa spp., Musaceae) Slices with the Method of Image Processing and Artificial Neural Networks

In this study, modeling of thin banana slices dried on 316 stainless steel shelves is carried out in an oven working with serial controlled and concentric blower-resistor couple. Changes occurred in banana slices (area and color) during drying process have been recorded by a camera. Additionally, weight has been measured with a load cell which is under the shelf and energy consumption has been measured with electricity consumption meter which is tied to energy input. The main aim of the study is to conduct the drying process of banana slices according to the data obtained from camera. Besides, obtained data have been tested with a powerful modeling technique like Artificial Neural Networks (ANN), and it has been seen that drying process could be modeled according to the data obtained from camera. Energy consumption data have been added in order to increase the performance of ANN and strengthen the modeling. Thus, an automatic drying system that can learn by itself using only a camera without any other sensors will be installed. This has been caused an increase in performance. However, it is obvious that it increases cost. According to the results of modeling process, 99% of “goodness of fit” has been obtained by using the change in banana slices and the number of pixels. It has been found that the developed model performed adequately in predicting the changes of the moisture content. Thus, it has been available to control the food drying process with a digital camera.

Application of Ozone against the Larvae of Plodia interpunctella (Hübner) and Its Impacts on the Organoleptic Properties of Walnuts

ABSTRACT

One of the most important products grown in Iran is walnut (Juglans regia L.), but it can be threatened by storage pests such as insects. Ozonation is an environmentally friendly method for killing pest insects; accordingly, ozone efficacy in the control of a pest species of walnut, Indian meal moth (Plodia interpunctella (Hübner)), was assessed in this research. The selected walnut samples were infested with larvae of Indian meal moth and then subjected to various combinations of ozone concentration (3, 4.5, and 6 ppm) and exposure time (20, 30, 40, and 50 min). After exposure to the treatment combinations, larval mortality rates and changes to the sensory properties (color, taste, smell, crispness, stiffness, and overall acceptability), indicating consumer preference, of the walnuts were evaluated. Our results revealed enhanced mortality rates of P. interpunctella with an increase in both ozone concentration and exposure time: 99% mortality was recorded at the concentration and exposure time of 6 ppm and 50 min, respectively. Sensory assessments of the samples showed that ozone treatments had no significant impacts on the color, taste, crispness, stiffness, and overall acceptability of the product. Also, few changes were recorded for its smell, which could be improved over time after being exposed to the air. We conclude that application of higher ozone concentrations might provide acceptable levels of insect pest control for stored walnuts with no associated reduced trade-off for their quality attributes.

HIGHLIGHTS

Evaluation of Microwave and Ozone Disinfections on the Color Characteristics of Iranian Export Raisins through an Image Processing Technique

Raisins are one of the most important Iranian export products but are threatened by various storage pests. Because of the disadvantages of fumigants, we evaluated alternative microwave and ozone methods for their disinfection and the side effects on raisin qualities. To perform microwave disinfection, the studied raisin samples were exposed to microwave powers of 450, 720, and 900 W for 20, 30, 40, and 50 s. Also, ozone treatments included various combinations of ozone concentrations (2, 3, and 5 ppm) and exposure times (15, 30, 45, 60, and 90 min). An image processing technique was implemented to determine the color changes of raisins in terms of lightness, redness, yellowness, total color difference, chroma, and hue angle. The results revealed that increasing the microwave power and exposure time might lead to further changes of the previously mentioned color characteristics. Compared with the microwave treatments, ozone treatments had fewer effects on those features. Generally, microwave and ozone methods could successfully disinfect Oryzaephilus surinamensis in raisins, with acceptable changes in all the color characteristics. Hence, the previously mentioned methods are proposed as alternative chemical fumigants.