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Grains are generally stored in warehouses after harvesting often for months. These stored grains are distributed to consumers through distribution channels as and when required. During such long storage, grains are affected by pests/insects and there can be considerable amount of storage losses. Pest infestation is highly affected by the temperature and relative humidity of the warehouse. In order to control such loss, a process called fumigation is performed to kill the pests by means of pesticides. The operation is hazardous and is carried out in buildings, soil and grain warehouses. Fumigation is also done during the process of export and import of goods to prevent the transfer of unusual organisms (Saeung et al., 2018 5th International conference on industrial engineering and applications (ICIEA), Singapore, 2018, pp 179–183 ( Saeung, P., Santalunai, S., Thosdeekoraphat, T., Thongsopa, C.: Improved efficiency of insect pest control system by SSPA. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, pp. 179–183 (2018). 10.1109/IEA.2018.8387092)). Phosphine (PH3) gas is mostly used presently to carry out fumigation in warehouses. This is important to know the performance of the fumigation process. But phosphine being highly toxic in nature, the warehouse is kept sealed during the entire fumigation period. Hence, the efficacy of fumigation during the process cannot be known. This paper describes an indirect way of checking the effectiveness of fumigation by measuring the concentration of fumigant in real time. A device named FUMON (fumigation monitoring system) is designed and developed for the said purpose. FUMON device automatically sniffs fumigant from the grain stack under fumigation at certain predefined interval of time. A PC/laptop connected to the FUMON device continuously plots the concentration (C) against time (T) and calculates the CT product value at every instance of time. The CT product value indicates the effectiveness of ongoing fumigation and seeing the value, the warehouse manager may take decision to lengthen or shorten the fumigation. Validation of the developed FUMON device is also done at Indian Institute of Food Processing Technology (IIFPT), Thanjavur.

In this paper, we present an efficient technique of electronic tongue (ET) signal processing using the sparse decomposition method for the prediction of tea quality. Sparse model coefficients of an under complete dictionary are considered as characteristic attributes of the ET signals obtained for characterization of tea samples. The effectiveness of the proposed method is established by performing experiment on three different types of pulse voltammetry. The ability of the sparse coefficients, as features of ET signal, is tested by employing three different classifiers. High classification accuracy of all the classifiers on three types of voltammetric measurement data validates the usefulness of this technique. The present model-based the tea quality prediction method is very fast, simple, and straightforward. This tea quality prediction method is able to address the problem more efficiently in comparison to other techniques found in the literature.

The colorimetric sensing technology has evolved into an essential tool for high-throughput analysis including portability and cost-effectiveness among available biomedical and agricultural screening approach. In this endeavor, the objective of work is to focus on the development of a field-portable instrument based on an Uniform Illumination Imaging System (UIIS), which will facilitate the colorimetric biochemical sensing. The developed field-portable, wavelength independent UIIS has been exploited for (a) rotavirus detection using commercial enzymatic immunoassay based microplate kit; (b) pesticide residue detection and quantification; The proposed system exhibited a good correlation in comparison to another two conventional techniques, i.e., multi-plate reader (r = 0.9991938) and LC-MS/MS (r = 0.998877399) with a short analysis time of 5 min for 95 test samples. Moreover, the feasibility of UIIS system has also been explored as field-portable enzyme-linked immunosorbent assay (ELISA) plate reader. By incorporating the Mahalanobis distance calculation, the advanced algorithm has been investigated and developed to analyze the data. The overall dataset was transformed into a matrix format to give a good correlation with a conventional plate reader, i.e., r = 0.915389612. Internet of things (IoT) enabled decision support system can be exploited by using big data analytics. Finally, test results can be shared with concerned stakeholders and the remote users. Thus, the developed UIIS will help to identify potential public health threats expeditiosly compared to conventional time consuming process of sample submission to the laboratory for analysis.

In this article, wavelet energy feature (WEF) has been extracted from the responses of e-nose and e-tongue for the classification of different grades of Indian black tea. For WEF, different decomposition levels of wavelet packet transform have been tested for both the systems and performance is evaluated with K- Nearest Neighbors classifier. Energy features of the best-suited decomposition level for e-nose and e-tongue have been calculated and fused to get a combined sensor response. Results confirm that the clustering nature (PCA plot) and classification accuracy (10-fold cross-validated based on KNN) have improved (accuracy 99.75%) with the applied method on the combined data. Moreover, this method is compared with some benchmark classification methods namely PLS-DA and Sammon's projection method which exhibits the superiority of the WEF extraction method combining the responses of multi sensory system.