Knowledge Bank

Traditional methods for analyzing the maturity of strawberry fruit have constraints due its destructive nature and time consumption. Recent advances in miniaturized optical technology have enabled the development of handheld, inexpensive sensor solutions based on cutting-edge Micro-Electro-Mechanical Systems (MEMS) technology. This fundamental innovation has enabled a new generation of infrared spectrometers for integration into handheld-size sensor systems used in field measurements at low cost. The goal of this study is to develop prediction algorithms for monitoring key compounds (soluble solids, titratable acidity, target specific organic (malic and citric) acids, and pigment) in intact strawberry fruits using the spectra obtained from a handheld Fourier transform near infrared (FT-NIR) device. Strawberry fruit samples (n~250) of different commercial brands were purchased from local grocery stores and their FT-NIR spectra collected. The soluble solids (refractive index), acidity (titration), organic acid profiles (high-performance liquid chromatography (HPLC)), and anthocyanin (UV-Vis spectrophotometry) levels of strawberries were determined by reference methods, and pattern recognition analysis (Partial Least Squares Regression, PLSR) was used to develop prediction algorithms. We found a large compositional diversity in the samples, with levels of soluble solids (5.1 – 10.5 °Brix), titratable acidity (0.4 – 1.0 g/100g), citric (0.4 – 0.9 g/100g) and malic (2.9 – 188 mg/100g) acids, and anthocyanins (0.0 – 32.6 mg/100g). PLS regression models using the spectral region between 1350 and 2500 nm gave correlation coefficients ranging from 0.89 (anthocyanin) to 0.97 (Brix) and low prediction errors that would allow for quality assurance applications. Our outcomes are comparable to or have outperformed results in the literature using similar or related devices, i.e., benchtop FT-NIR, hyperspectral imaging, and Visible-Short Wave Near Infrared technologies. By using a handheld FT-NIR device, our prediction algorithms would provide critical quality information quickly (10 sec) and in the field, decreasing the time and cost of testing strawberry fruits and standardizing the quality of fruit delivered to consumers. Widespread adoption of these smart and easy-to-use handheld systems will improve the strawberry industry’s ability to make informed decisions to enhance the quality reaching consumers.