Use of FTIR spectroscopy integrated with multivariate chemometrics as a swift, and non-destructive technique to detect various adulterants in virgin coconut oil: A comprehensive review

Virgin coconut oil (VCO) has of late come out as one of the most treasured edible oils as it has many functions as a crucial component used in food preparation, pharmacy, and cosmetic merchandise. These quality parameters have placed VCO in high consumption need, and the chances of its adulteration have significantly surged. Therefore, adulteration determination is the most demanding element for the oil-consuming population, oil-producing firms, and authorities. The typical analytical ways are generally arduous, prolonged, pernicious, require lengthy sample processing, and lack online tracking and monitoring. Contemporary oil-producing facilities require a rapid, non-destructive analytical method for the effective finding of VCO mixing with its blends. Fourier transform infrared (FTIR) spectroscopy with multivariate chemometrics, is an exquisite method for the expeditious revelation of numerous blends in VCO. The crucial spectral region of FTIR analyzed with numerous differentiation and quantification chemometrics means led to the revelation of the lowest concentration of numerous blends of VCO with accurate and precise results. Hence, FTIR spectroscopy, united with numerous multivariate chemometric methods, can be implemented as an exemplary methodology for different types of blend detection and quantification in VCO

Expeditious and accurate detection of palm oil adulteration in virgin coconut oil by utilizing ATR-FTIR spectroscopy along with chemometrics and regression models

Virgin coconut oil (VCO), being one of the most treasured and healthy edible oil, is at great risk of blending with cheaper oils like palm oil (PO). Fourier transform infrared (FTIR) spectroscopy with an Attenuated total reflection (ATR) accessory was used together with multivariate chemometrics for the classification and evaluation of numerous concentrations of PO (0.5–30% v/v) in VCO. Linear Discriminant Analysis (LDA) showed a 100% correct classification for both the initial and cross-validation groups for all the subsets of PO blends. Principal components regression (PCR) and Partial least squares regression (PLS-R) calibration models were generated and compared for normal, 1st, and 2nd derivatives of the combined informational spectral domain (3010–2800 cm−1 & 1800–700 cm−1) and separate informational domains of the spectra 3010–2800 cm−1 and 1800–700 cm−1 to get the best-fitting models. Regression models for the 2nd derivatives of 1800–700 cm−1 informational spectral domain gave exquisite outcomes of prediction with immense accuracy and precision with the highest R2 of 0.998, and the root mean square error of prediction (RMSEP) of 0.375% v/v for PLS-R and R2 of 0.998 and RMSEP of 0.441% v/v for PCR respectively. The lowest detectable limit of PO in VCO was estimated as 0.5% v/v. Hence, it has been concluded that PO adulteration of up to 0.5% with VCO can be quickly determined by using ATR-FTIR spectroscopy fitted with multivariate chemometrics

Data_Sheet_1_Multi-location evaluation of mungbean (Vigna radiata L.) in Indian climates: Ecophenological dynamics, yield relation, and characterization of locations.doc

Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.</p

Abstracts of AICTE Sponsored International Conference on Post-COVID Symptoms and Complications in Health

This book presents the selected abstracts of the International Conference on Post-COVID Symptoms and Complications in Health, hosted from the 28th to 29th of April 2022 in virtual mode by the LR Institute of Pharmacy, Solan (H.P.)-173223 in Collaboration with AICTE, New Delhi. This conference focuses on the implications of long-term symptoms on public health, ways to mitigate these complications, improve understanding of the disease process in COVID-19 patients, use of computational methods and artificial intelligence in predicting complications, and the role of various drug delivery systems in combating the complications. Conference Title:  International Conference on Post-COVID Symptoms and Complications in HealthConference Sponsor: AICTE, New Delhi.Conference Date: 28-29 April 2022Conference Location: OnlineConference Organizer: LR Institute of Pharmacy, Solan (H.P.)-173223

Abstracts of AICTE Sponsored International Conference on Post-COVID Symptoms and Complications in Health

This book presents the selected abstracts of the International Conference on Post-COVID Symptoms and Complications in Health, hosted from the 28th to 29th of April 2022 in virtual mode by the LR Institute of Pharmacy, Solan (H.P.)-173223 in Collaboration with AICTE, New Delhi. This conference focuses on the implications of long-term symptoms on public health, ways to mitigate these complications, improve understanding of the disease process in COVID-19 patients, use of computational methods and artificial intelligence in predicting complications, and the role of various drug delivery systems in combating the complications. Conference Title:  International Conference on Post-COVID Symptoms and Complications in HealthConference Sponsor: AICTE, New Delhi.Conference Date: 28-29 April 2022Conference Location: OnlineConference Organizer: LR Institute of Pharmacy, Solan (H.P.)-173223