Extending shelf life of food products is a major concern of the producers, and the food industry requires ‘greener’ alternatives to the current technologies. Ozone-based food preservation may suit ...

Ozone based food preservation: a promising green technology for enhanced food safety

Background Consumption of fruits and vegetables is an indispensable component of human dietary preference, however; it is unfortunate that the enormous pesticide residues remain in the plant produces. Pesticides used to control the pests and diseases of the crops and livestock and its spill-over in the food production system has been an inevitable consequence. Researchers have been taking persistent efforts to eliminate pesticide residues in the food to make it safe for human consumption. Conventional techniques such as the washing of agricultural produces with chemicals, peeling, salting, and using different agents have not been an efficient means of pesticide removal. Scope and approach In recent times, the emergence of non-thermal technology such as the ozone to degrade the pesticide residues is of great utility in the food processing industries. It is also considered as a green technology because unlike other conventional methods ozone treatment leaves little residual traces. However, some of the discrepancies and challenges in using this technology require to be addressed to increase its efficiency. The objective of the current review is to provide a comprehensive and critical view of the use of ozone in pesticide residue dissipation in various food matrixes. Key findings and conclusion Analysis of the pros and cons of ozone treatment reveals it as a potential technique for the degradation of pesticide residues. The diversity and complexity of pesticides along with the inherent differences in their chemical structures and residue levels of pesticides in agro-products are the factors that require due consideration. Furthermore, processing and operating conditions of the ozone treatment are some of the other major determinants to improve the efficiency of ozone treatment for the degradation of pesticides.

Ozone as a novel emerging technology for the dissipation of pesticide residues in foods–a review

Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

Ozone is activated oxygen and it is referred to as a triatomic form of oxygen. It is a natural agent and has a broad antimicrobial property, which together with an oxidation potential, make it an attractive option for the food industry. This article focuses on the use of ozone for preservation of vegetables, fruits and fruit juices, and highlights the inactivation mechanism of microorganisms. The application of ozone in grain storage and the quality of ozone treated grains is discussed, along with the reaction kinetics of ozone in grains.

Application and Kinetics of Ozone in Food Preservation

Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.

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Assessment of seed quality using non-destructive measurement techniques: a review

Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy

Quantification of browning in apples using colour and textural features by image analysis

Image processing in agriculture relies primarily on correlating color changes within images’ region of interest to specific quality attributes (e.g., plant phenology, plant health, crop stress, maturity). Changes in lighting conditions during image acquisition affect image color, even though there is no change in quality, and produces misleading inference when used without calibration. The focus of this study was to develop a method for calibrating images to make them homogeneous to improve phenological comparisons. The method was developed with synthetic images and validated with actual plant images in laboratory and field conditions using a standard ColorChecker (X-Rite) chart. Six different color schemes were tested to determine the effect of patch order, and minimum number of patches required for efficient calibration. A user-coded ImageJ plugin named ‘ColorCal’ was developed in Fiji package for color calibration that derived and applied a [ 3 × 3 ] color calibration matrix, based on selected color patches and standard values. Modified total error and calibration performance index (CPI) were developed to evaluate calibration performance. Calibration using any 12 color patches taken in any order gave equal performance ( 0.14 ⩽ CPI ⩽ 0.26 ). Calibration performance using only commonly followed neutral color patches (e.g., white, gray) was poor ( 0.26 ⩽ CPI ⩽ 1.0 ). Using red (R), green (G), and blue (B) color patches was recommended as it produced visually similar images, the performance was comparable with 24 color patches ( 0.21 ⩽ CPI ⩽ 0.24 ), and was simple and practical. The developed plugin took ≈7 s for calibration (Windows laptop, Intel Core i5, and 8 GB RAM). Determining phenological and other applications using the plugin was more reliable than using the raw images.

S. Sunoj

Papers

Application and Kinetics of Ozone in Food Preservation

Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy

Quantification of browning in apples using colour and textural features by image analysis

Color calibration of digital images for agriculture and other applications

Modeling of coconut milk residue incorporated rice‐corn extrudates properties using multiple linear regression and artificial neural network