Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

INTRODUCTION: Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. OBJECTIVE: The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. METHODS: Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. RESULTS: The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. CONCLUSION: The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Rapid estimation of piperine in black pepper: Exploration of Raman spectroscopy.

INTRODUCTION: The major chemical marker of black pepper (Piper nigrum L) is piperine and its estimation is extremely important for quality assessment of black pepper. The methods for piperine quantification, to date, are laboratory based and use high end instruments like chromatographs, which require tedious sample processing and cause sample destruction. OBJECTIVES: In this article, we present a simple, rapid and green analytical method based on Raman spectroscopy for the quantitative assessment of piperine. MATERIAL AND METHODS: To assess the potential of the technique, we report the complete vibrational characterisation of the piperine with density functional theory (DFT) calculations. RESULTS: The theoretical peaks were obtained at 1097 cm-1 , 1388 cm-1 , 1528 cm-1 , 1578 cm-1 , and at 1627 cm-1 , and this result was verified in a Raman spectrometer followed by a preliminary experiment. Twenty black pepper samples were analysed using high-performance liquid chromatography (HPLC) and used as reference data for Raman analysis. The Raman shift spectra were analysed using partial least squares (PLS) and good prediction accuracy with correlation coefficient of prediction (Rp2 ) = 0.93, root mean square error of prediction (RMSEP) = 0.13 and residual prediction deviation (RPD) = 3.9 obtained. CONCLUSIONS: The results demonstrate the efficacy of the Raman technique for the estimation of piperine in the dry fruit of Piper nigrum.

Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy.

This paper reports on the development of an integrated leaf quality inspecting system using near infrared reflectance (NIR) spectroscopy for quick and in situ estimation of total polyphenol (TP) content of fresh tea leaves, which is the most important quality indicator of tea. The integrated system consists of a heating system to dry the fresh tea leaves to the level of 3-4% moisture, a grinding and sieving system fitted with a 250 micron mesh sieve to make fine powder from the dried leaf. Samples thus prepared are transferred to the NIR beam and TP is measured instantaneously. The wavelength region, the number of partial least squares (PLS) component and the choice of preprocessing methods are optimized simultaneously by leave-one-sample out cross-validation during the model calibration. In order to measure polyphenol percentage in situ, the regression model is developed using PLS regression algorithm on NIR spectra of fifty-five samples. The efficacy of the model developed is evaluated by the root mean square error of cross-validation, root mean square error of prediction and correlation coefficient (R2) which are obtained as 0.1722, 0.5162 and 0.95, respectively.

Quantification and Standardization of Andrographolide in Andrographis Paniculata Samples by Validated RP-HPLC and HPTLC Methods.

Andrographis paniculata (family Acanthaceae) is known as Kalmegh, one of the traditionally used important medicinal plant contains several biologically active phytochemical including andrographolide. A. paniculata is broadly used by healthcare practitioners in India and also used in different traditional medicinal system. In this study, the leaves of A. paniculata were collected from West Medinipur, East Medinipur, South 24 Parganas, Purulia and Hooghly district of West Bengal, India. This study aiming towards validation and development of a simple, precise and reproducible reverse-phase high-performance liquid chromatography (RP-HPLC) and high-performance thin layer chromatography (HPTLC) methods for quantification of andrographolide in A. paniculata extracts. The validated RP-HPLC and HPTLC study confirmed that different concentrations of andrographolide content present in the plant samples, which are collected from above different districts of West Bengal, India. The amounts of andrographolide were found to be 2.71% (w/w), 3.19% (w/w), 1.83% (w/w), 1.73% (w/w) and 2.94% (w/w) in RP-HPLC study and 2.13% (w/w), 2.51% (w/w), 1.01% (w/w), 1.25% (w/w) and 2.15% (w/w) in HPTLC study. This precise, reproducible, accurate and specific method can be used for the quantification of andrographolide in kalmegh, as per the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines recommendations.

Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine.

Andrographis paniculata (Burm. F) Nees, has been widely used for upper respiratory tract and several other diseases and general immunity for a historically long time in countries like India, China, Thailand, Japan, and Malaysia. The vegetative productivity and quality with respect to pharmaceutical properties of Andrographis paniculata varies considerably across production, ecologies, and genotypes. Thus, a field deployable instrument, which can quickly assess the quality of the plant material with minimal processing, would be of great use to the medicinal plant industry by reducing waste, and quality grading and assurance. In this paper, the potential of near infrared reflectance spectroscopy (NIR) was to estimate the major group active molecules, the andrographolides in Andrographis paniculata, from dried leaf samples and leaf methanol extracts and grade the plant samples from different sources. The calibration model was developed first on the NIR spectra obtained from the methanol extracts of the samples as a proof of concept and then the raw ground samples were estimated for gradation. To grade the samples into three classes: good, medium and poor, a model based on a machine learning algorithm - support vector machine (SVM) on NIR spectra was built. The tenfold classification results of the model had an accuracy of 83% using standard normal variate (SNV) preprocessing.

GPS Tracking App for Police to Track Ambulances Carrying COVID-19 Patients for Ensuring Safe Distancing.

The outbreak of the SARS-CoV-2 virus is causing loss of lives and property all over the world. There have been more than 2.1 million cases of COVID-19 with a death of more than 1.2 lakh patients worldwide and the numbers are still rising. The virus spreads rapidly by the droplets coming out from the nose and mouth of an infected person (Sandoiu in Why does SARS-CoV-2 spread so easily? Medical news today, 2020 https://www.medicalnewstoday.com/articles/why-does-sars-cov-2-spread-so-easily). In this situation, proper quarantining and monitoring of the already infected patients are very essential. In cases where patients need to be transferred to different locations by ambulances, monitoring of these ambulances by the traffic police can help to ensure distancing and faster movement of the vehicle inside the city. This paper presents the development of a Real-time Global Positioning System-based tracking app for the ambulances carrying COVID-19 patients which would help traffic police to ensure distancing the patients from the public.

Development of An Android Application for Viewing Covid-19 Containment Zones and Monitoring Violators Who are Trespassing into It Using Firebase and Geofencing.

The World Health Organization has declared the outbreak of the novel coronavirus, Covid-19 as pandemic across the world. With its alarming surge of affected cases throughout the world, lockdown, and awareness (social distancing, use of masks etc.) among people are found to be the only means for restricting the community transmission. In a densely populated country like India, it is very difficult to prevent the community transmission even during lockdown without social awareness and precautionary measures taken by the people. Recently, several containment zones had been identified throughout the country and divided into red, orange and green zones, respectively. The red zones indicate the infection hotspots, orange zones denote some infection and green zones indicate an area with no infection. This paper mainly focuses on development of an Android application which can inform people of the Covid-19 containment zones and prevent trespassing into these zones. This Android application updates the locations of the areas in a Google map which are identified to be the containment zones. The application also notifies the users if they have entered a containment zone and uploads the user's IMEI number to the online database. To achieve all these functionalities, many tools, and APIs from Google like Firebase and Geofencing API are used in this application. Therefore, this application can be used as a tool for creating further social awareness about the arising need of precautionary measures to be taken by the people of India.