Lateral flow immunoassay-based absolute point-of-care technique for authentication of meat and commercial meat products.

Point-of-care (POC) assay is an emerging technique for rapid initial screening of meat fraud incidents in a resource-limited environment. To achieve this goal, a simple extraction protocol is proposed for efficient recovery of meat proteins from raw, heat-processed, and commercial samples as well as meat offals without utilizing sophisticated laboratory settings. A sandwich-format lateral flow immunoassay (LFIA) was developed based on gold nanoparticles as labels and immunoglobulins (IgG and IgY) as biomarkers for meat species identification in raw and cooked meat mixes. The test system showed a sensitivity of 10 ng/mL allowing the detection of as low as 0.063% pork and chicken meat and 0.125% sheep meat (lamb) in meat mixes within 15 min including sample preparation. Reproducibility of the assay was confirmed by the fully consistent intra- and inter-laboratory tests and RT-PCR method. The current study developed a field-deployable extraction technique and highly-specific, sensitive, reproducible, cost-effective, and user-friendly LFIA-based assay for rapid species authentication in raw, cooked, and commercial meat samples and meat offals. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05663-2.

Prediction of COVID-19 cases using the weather integrated deep learning approach for India.

Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID-19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.