Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry.

Yanhua Lai,Da Wang,Wenwen Wei,Xiangzheng Yang,Di Wu,Lianwen Jia,Shaojia Li

doi:10.1155/2019/2360631

Abstract

The quality of strawberry powder depends on the freshness of the fruit that produces the powder. Therefore, identifying whether the strawberry powder is made from freshly available, short-term stored, or long-term stored strawberries is important to provide consumers with quality-assured strawberry powder. Nevertheless, such identification is difficult by naked eyes, as the powder colours are very close. In this work, based on the measurement of near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectra of strawberry powered, good classification results of 100.00% correct rates to distinguish whether the strawberry powder was made from freshly available or stored fruit was obtained. Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. Optimal variables were selected by successive projections algorithm (SPA), uninformation variable elimination, and competitive adaptive reweighted sampling, respectively. The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. The results of this work confirmed the feasibility of using NIR and MIR spectroscopic techniques for rapid identification of strawberry powder made from freshly available and stored strawberry.

Highlights

Strawberries are known to be a natural source of phenols and other bioactive substances in the world of phytonutrients
Partial least squares discriminant analysis (PLS-DA) and least squares support vector machines (LS-SVM) were used to establish classification models. e reference values of the dependent variable were set −1 and 1 for freshly available and stored fruit, respectively. e results show that when NIR data was considered, the PLS-DA model had the correct rates of 95.95% and 94.59% for calibration set and prediction set, respectively
On the contrary, when LS-SVM was used for the model calibration, both NIR and MIR data had the correct rate of 100.00% for both calibration set and prediction set. e above results show that both NIR and MIR could identify strawberry powder made from freshly available and stored fruit

Summary

Introduction

Strawberries are known to be a natural source of phenols and other bioactive substances in the world of phytonutrients. Strawberry powder obtained by freeze-drying retains most nutrients of fresh strawberries and is an excellent source of dietary fiber, antioxidants, and vitamin C. Strawberry powder as diet supplements shows health benefits. In a trial with healthy human subjects, a 3-week dietary intervention with strawberry powder reduced plasma concentrations of cholesterol and small HDL-cholesterol particles and increased LDL particle size in obese subjects [1]. In the highfat diet-induced obese C57BL/6 mice, strawberry powder decreased the blood glucose level and lowered the plasma C-reactive protein [2]. Ese results indicated a potential role of strawberry powder on reducing the risks associated with obesity and diabetes. The Journal of Analytical Methods in Chemistry anthyocyanins in the strawberry powder was considered as the functional components in reducing obesity [3]

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Results

Conclusion