Christopher Ball

We evaluated the capability of near-infrared (NIR) transflectance spectroscopy coupled with multivariate analysis to develop predictive regression models that quantify taste indicators, i.e., soluble solids, titratable acidity (TA), citric acid, sucrose, glucose, and fructose, in orange juice (sample size = 123). The NIR spectra were collected in transflectance mode using a handheld scanner with a series of reflectors that provide three distinct pathlengths (0.50 mm, 0.80 mm, and 2 mm) across the juice samples. Reference data were collected using automatic titrators for TA, refractometer for Brix, and high-performance liquid chromatography (HPLC) for organic acids and sugars. Orange juice samples represented a diverse genetic pool of Hamlin and Valencia varieties harvested at different maturity stages providing a unique range of concentration for each parameter (Brix, 4.3–12.3%, and TA 0.8–3.5 g citric acid/L). Pattern recognition analysis correlated the spectral data to reference values using partial least square regression. Results showed better performance using 0.50 mm and 0.80 mm pathlengths, R _pre ≥ 0.82, root mean square error of prediction (RMSEP) range 0.14–6.87, residual prediction deviation (RPD) range 1.2–8.3, residual error rate (RER) range 2.7–30 compared to the 2 mm pathlength (R _pre ≥ 0.72, RMSEP range 0.27–4.47, RPD range 1.2–1.7, RER range 5.2–7.8). We demonstrated that a field deployable NIR scanner can provide reliable prediction using a transflectance approach using algorithms that were synchronized for cloud computing using R programming, providing easy accessibility for analysis. This technology offers orange breeders and growers an affordable, rapid (10 s), and accurate solution for in-field, real-time monitoring of taste indicators in orange juice, which can help expedite critical decision-making processes in the field (harvest timing, process optimization, and aid in breeding research).

In an infrared photodetector, noise current (dark current) is generated throughout the volume of the detector. Reducing the volume will reduce dark current, but the corresponding smaller area will also reduce the received signal. By using a separate antenna to receive light, one can reduce the detector area without reducing the signal, thereby increasing its signal-to-noise ratio (SNR). Here, we present a dielectric resonator antenna (DRA)-coupled infrared photodetector. Its performance is compared to a conventional resonant cavity-enhanced slab detector. The noise equivalent power (NEP) is used as a figure of merit for the comparison. Formulas for the NEP are derived for both cases. A pBp photodiode detector is assumed in the comparison. The active region of the photodiode is an InAs/GaSb Type II Superlattice (T2SL). A Genetic Algorithm is used to optimize the dimensions of the detector and the DRA to achieve the smallest NEP. The result is a photodetector that converts over 85% of the incident light into carriers with a volume reduced by 95%. This optimal geometry leads to an NEP reduced by 6.02 dB over that of the conventional resonant cavity-enhanced slab detector.

This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (R_pre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (R_Pre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.