Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms.

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration.

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (Rp = 0.937, RPD = 3.38) and CP (Rp = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Fraud detection in crude palm oil using SERS combined with chemometrics.

Edible crude palm oil (CPO) is a vital oil utilized in various industries, including food, pharmaceuticals, and domestic cooking. Unfortunately, reports of CPO adulteration with harmful Sudan dyes have surfaced over the years. Surface-enhanced Raman spectroscopy (SERS) and chemometrics were employed to detect Sudan dyes adulteration in CPO within 900 - 1800 cm- 1 Raman peak. The concentration of Sudan dyes detected in CPO samples ranged between 0.005 and 4 ppm. The principal component analysis (PCA) model detected Sudan II and Sudan IV in CPO with 99.88 and 99.90% accuracy. Linear discriminant analysis (LDA) and K-Nearest Neighbors (KNN) also recorded high detection rates of Sudan II and IV dyes in CPO. Sudan II and IV dyes could be detected at 0.0028 ppm and 0.0019 ppm by this sensor. The performance of the Au@Ag SERS sensor was comparable to that of HPLC. This study proved SERS and chemometrics can be used to authenticate edible CPO.

Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis.

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.

Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review.

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.