Mycotoxigenic fungi contamination of grains and peanuts from open markets in Kelantan, Malaysia

The warm weather and high relative humidity in Malaysia are ideal for the survival and proliferation of mycotoxigenic fungi leading to a high rate of stored product contamination. This study was conducted to enumerate and characterise the mycotoxigenic fungi associated with commonly consumed food grains in Kelantan, Malaysia. The fungal bioburden and fungal identification from forty-four composite food samples comprising 11 samples each of maize, wheat, rice, and peanuts from open markets in Kelantan, Malaysia, were determined using standard mycological techniques. A total of 115 mould fungal isolates belonging to 12 species were isolated, of which Aspergillus flavus (17.39%), A. versicolor (13.04%), A. felis (12.17%), Neoscytalidium dimidiatum (11.3%), Penicillium cheresanum (11.3%) and P. chrysogenum (8.7%), were predominant. Peanuts were the most contaminated (9.7×105 ± 1.5×105 CFU/g) followed by maize (7.5×105 ± 1.8×106 CFU/g), wheat (1.9×105 ± 2.6×105 CFU/g), and rice (9.9×104 ± 1.5×105 CFU/g). The levels of the mycotoxigenic fungi in peanut, maize, and wheat were above the permissible limit of 102 CFU/g set by the Malaysian Ministry of Health and 102 to 105 CFU/g set by the International Commission for Microbiological Specification for Foods, signifying that they are unsafe for use as food or feed ingredients. Hence, there is a need for more stringent control measures.

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

Hermetic Storage Technology for Food Grains: New Beginning of Organic and Safe Storage for Farmers and Consumers: A Review

The moisture content of grains, oxygen and carbon dioxide concentration in storage environment affects the storage life of food grains. The moisture content affects on storability of food grains severely, hence the storage structures must be provided with sufficient protection from moisture exchange between grain and atmosphere. The insect and pests grows very fast in presence of oxygen and moisture in the storage environment. The humid climate provides favorable conditions to moulds and insects to grow in most of the food grains and oilseeds. The grains exposed to these favorable environment further not useful for human consumptions. Many harmful chemicals like aflatoxins are formed in oilseeds and cereals. The emerging hermatic storage is highly effective technology to prevent the spoilage of grains without use of any harmful fumigants and chemicals, hence it is popularly known as organic storage technology. In this technology the flow of oxygen and water from external environment is fully controlled by impermeable, triple layer hermatic plastic bags.

Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.

Introduction

India has come a long way from being a food scarce nation in the 1960s to a food surplus nation thereafter. The remarkable transformation of the agricultural sector was the result of massive improvements in productivity level owing to the Green Revolution in the case of cereals and the breakthrough that followed in few other agricultural commodities, most notably, dairying. Today, India is the largest producer of milk, pulses, banana, mango, pomegranate, papaya, lemon, okra, ginger and non-food crops like cotton and jute; the second-largest producer of rice, wheat, fruits and vegetables, tea and one of the leading producers of eggs and meat in the world. India produced 281.8 million tonnes of food grains, 307.7 million tonnes of horticulture crops, 176.5 million tonnes of milk, 96 billion eggs and 7.7 million tonnes of meat during TE 2018–19.

Grapes and Pomegranate Value Chains

Horticulture has grown in size and importance within the agriculture landscape of the country. Horticulture production doubled from 191 million tonnes in 2006–07 to nearly 300 million tonnes in 2016–17. In fact, by 2015–16, production of horticulture crops was higher than that of food grains. Further, production of fruit crops as a proportion of horticulture crops increased from 29.5% in 2001–02 to 31.5% in 2015–16.

Crop Residue Based Complete Feed for Enhancing Livestock Performance- A Review

The estimated projected data regarding demand and supply of feed and fodder shows13.20% and 18.43% deficit between demand and supply of dry and green fodder to livestock, respectively. Shortage of land for cultivation of fodders and increased human as well as livestock population has led to heavy competition for food grains that necessitates using the crop residues for the feeding of livestock. These crop residues are rich in fibre and low in other nutrients; they also have low palatability and digestibility. The best way to efficiently utilize the crop residues is to prepare complete feeds from pretreated crop residues + concentrates mixture. Pretreatment of crop residues increases digestibility by increasing rate of delignification, increasing enzymatic hydrolysis of cellulose, hemicelluloses and thus increasing the glucose yield. The complete feed is a quantitative mixture of all dietary ingredients to provide the specific nutrient requirement for various physiological functions of livestock. Crop residue-based complete feed could be prepared in mash, block and pellet (Expander and extruder) form. In this system, all feed ingredients including roughages are proportioned, processed and mixed into a uniform blend. Different researchers undertook feeding trials of complete animal feed and conventional animal feed and reported better performance in terms of weight gain, milk yield and reproductive performance on feeding complete feed pellets or complete feed blocks to ruminants as compared to conventional ration. In conclusion, crop residue based complete feed could improve ruminant performance and reduces cost of feeding per kg gain or yield and thereby improving livestock based rural economy in developing countries like India.

Standards of freshness and shelf life of food grain of wheat

Приведены результаты изменения показателя кислотного числа жира (КЧЖ) продовольственного зерна пшеницы урожаев 2015 и 2018 гг. при длительном лабораторном хранении в условиях пониженных (+10 °С), умеренных (+20 °С), повышенных (+30 °С) температур и стандартной влажности для зерна пшеницы (не выше 14 %). Доказана возможность использования этого показателя для установления сроков безопасного хранения и годности зерна. Для определения норм свежести и годности продовольственного зерна пшеницы по нормам значения КЧЖ, разработанным ранее для пшеничной муки, были отобраны 35 проб зерна пшеницы с широким диапазоном значений КЧЖ и произведены лабораторные помолы муки высшего сорта. Установлена взаимосвязь показателя КЧЖ муки после созревания от показателя КЧЖ исходного зерна. По результатам статистической обработки полученной зависимости авторам удалось определить нормы свежести и годности продовольственного зерна пшеницы, которые составили 29 мг КОН на 1 г жира и 50 мг КОН на 1 г жира соответственно. The results of changes in the indicator of the acid number of fat (FAV) of food grain of wheat of the harvests of 2015 and 2018 are presented during long-term laboratory storage under conditions of low (+10 °C), moderate (+20 °C), high (+30 °C) temperatures and standard humidity for wheat grain (not higher than 14 %). The possibility of using this indicator has been proven to establish the terms of safe storage and shelf life of grain. To determine the norm of freshness and shelf life of food grains of wheat according to the norms of the FAV value developed earlier for wheat flour, 35 samples of wheat grains with the FAV values of 9.1 to 53.4 mg KOH per 1 g of fat were taken and laboratory grinding of the premium flour was made. The relationship between the FAV index of flour after ripening and the FAV index of the original grain has been established. Based on the results of statistical processing of this relationship, the authors were able to determine the norms of freshness and shelf life of food grain of wheat, which amounted to 29 mg KOH per 1 g of fat and 50 mg KOH per 1 g of fat, respectively.