Quantitative detection of pepper powder adulterated with rice powder using Fourier-transform near infrared spectroscopy

Abstract

Near infrared (NIR) spectroscopy model was developed for detecting pepper powder adulterated with rice powder. The adulterated pepper powder samples were prepared by mixing rice powders with pure pepper powder to 19 levels of concentrations (w/w) from 5-95%w/w. Two hundred ten NIR spectra of pure and adulterant pepper powders were recorded using Fourier-transform near infrared spectrometer. The NIRs quantitative model for detecting adulterant pepper were established using partial least squares regression (PLS). The optimum model was established from NIR spectra treated by constant offset elimination with the R v a l 2 of 0.99. These results show that the NIR spectroscopy could be a modern method for monitoring adulteration of pepper powder with rice powder.

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

“…These techniques are also known as machine learning in the field of computer science. The combination of NIR spectroscopy and machine learning showed successful results for monitoring food adulteration in previous research such as honey [2][3][4][5], milk [6], pepper [7], sesame oil [8], Lonicerae Japonicae Flos [9], soybean oil [10], Panax notoginseng [11] and notoginseng [12]. However, the NIR spectrum consists of a great number of absorbance values on all wavenumber range reaching thousands of variables.…”

Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics

“…developed FTNIRS‐PLSR models in two different wavelength regions of 1400–1550 and 1900–2050 cm −1 with R 2 of 0.91–0.99 and RMSEC of 0.23–1.3% quantitative detection of starch (1–30%; w/w ) in turmeric powder. Lapcharoensuk et al 24 . developed PLS model with R 2 of 0.99, root mean square error of prediction (RMSEP) of 1.68, bias of 0.059% for the detection of rice powder adulteration (5–95%) in pepper samples using FTNIRS.…”

“…RPD value > 8 and < 2.3 represents excellent and poor performance of the developed model, respectively 15 . Repeatability was tested under the same measurement conditions for presenting the variation in the scans of NIRS instrument by testing the same sample with known adulterant level using ten replications of NIR scanning 24 . Reproducibility was checked by using known concentration of sample prepared by three different laboratory personnel and prediction was noted.…”

“…18 The spectra of these bands are affected by their environment and can give fine details of the chemistry of molecules present in a given sample. There are a number of reports where near-infrared spectroscopy (NIRS) has been used to detect different adulterants in a wide range of food products, namely common wheat flour, cassava flour, corn flour in organic Avatar wheat flour, 19 metanil yellow in turmeric, 20 starch in onion powder and turmeric, 21,22 papaya seeds adulterated in black pepper samples, 23 rice powder in black pepper powder, 24 black pepper husk, spent materials, pinheads, papaya seeds, and chili powder in black pepper, 25 and green pea and peanut adulteration in pistachio. 26 As a rapid analytical tool, NIRS has emerged as a simple, non-destructive, chemical free, green chemistry approach in food quality control for detection of different adulterants in food commodities.…”

Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

“…Potential benefits of PAT adoption are considerable and include higher quality products, improved product consistency and reduced manufacturing costs (Pu, O'Donnell, Tobin, & O'Shea, 2020). Visible (Vis)/NIR spectroscopy is one of the most promising sensing approaches for in-line control of peppercorns preservation processing (Lapcharoensuk et al, 2019;Orrillo et al, 2019;Wilde, Haughey, Galvin-King, & Elliott, 2019). The advantages of spectroscopic methods for bioprocess monitoring are manifold and include real-time capability, nondestructive nature, ease of maintenance and the possibility for simultaneous determination of multiple target analytes (Zimmerleiter et al, 2019).…”