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Infrared spectroscopy (wavelengths ranging from 750–25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

Leaf water content (LWC) is a crucial physiological parameter that plays a limiting role in the efficiency of photosynthesis and biomass production in many plants. This study investigated the use of diffuse reflectance near-infrared spectroscopy (NIRS) for the rapid prediction of the gravimetric LWC in eucalypt leaves from Eucalyptus and Corymbia genera. The best-performing model for LWC gave a R2pred of 0.85 and RMSEP of 2.32% for an independent test set, indicating that the handheld NIR instrument could predict the LWC with a high level of accuracy. The use of support vector regression gave slightly more accurate results compared with partial least squares regression. Prediction models were also developed for leaf thickness, although these were somewhat less accurate (R2pred of 0.58; RMSEP of 2.7 µm). Nevertheless, the results suggest that handheld NIR instruments may be useful for in-field screening of LWC and leaf thickness in Australian eucalypt species. As an example of its use, the NIR method was applied for rapid analysis of the LWC and leaf thickness of every leaf found on an E. populnea sapling.

Although Australia is the largest exporter of faba bean globally, there is limited information available on the levels of bioactive compounds found in current commercial faba bean varieties grown in this country. This study profiled the phenolic acid and flavonoid composition of 10 Australian faba bean varieties, grown at two different locations. Phenolic profiling by HPLC-DAD revealed the most abundant flavonoid to be catechin, followed by rutin. For the phenolic acids, syringic acid was found in high concentrations (72.4–122.5 mg/kg), while protocatechuic, vanillic, p-hydroxybenzoic, chlorogenic, p-coumaric, and trans-ferulic acid were all found in low concentrations. The content of most individual phenolics varied significantly with the variety, while some effect of the growing location was also observed. This information could be used by food processors and plant breeders to maximise the potential health benefits of Australian-grown faba bean.

The rise of online modes of content delivery, termed e-learning, has increased student convenience and provided geographically remote students with more options for tertiary education. However, its efficacy relies upon student access to suitable technology and the internet, and the quality of the online course material. With the COVID-19 outbreak, education providers worldwide were forced to turn to e-learning to retain their student base and allow them to continue learning through the pandemic. However, in geographically remote, developing nations, many students may not have access to suitable technology or internet connections. Hence it is important to understand the potential of e-learning to maintain equitable access to education in such situations. This study found the majority (88%) of commencing students at the University of the South Pacific owned at least one ICT device and had access to the internet. Similarly, most students had adequate to strong ICT skills and a positive attitude toward e-learning. These attitudes among the student cohort, in conjunction with the previous experience of The University of the South Pacific in distance education, are likely to have contributed to its relatively successful transition from face-to-face to online learning as a result of the COVID-19 pandemic.

This study compared the performance of near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) for the prediction of moisture, protein, total phenolic content (TPC), ferric reducing antioxidant potential (FRAP) and total monomeric anthocyanin (TMA) content in 65 samples of Australian wheat flour. Models were constructed on 50 of the wheat samples, with the 15 remaining samples used as a dependent test set. NIRS showed excellent results for the prediction of protein content (R2test = 0.991; RMSEP = 0.22% w/v) and acceptable to good results for TPC (R2test = 0.83; RMSEP = 3.9 mg GAE/100 g), FRAP (R2test = 0.92; RMSEP = 5.4 mg TE/100 g) and moisture content (R2test = 0.76, RMSEP = 0.62% w/v). Similarly, MIRS showed the best results for protein prediction (R2test = 0.93, RMSEP = 0.62% w/v) and acceptable results for moisture content (R2test = 0.83, RMSEP = 0.65% w/v), FRAP (R2test = 0.83, RMSEP = 7.0 mg TE/100 g) and TPC (R2test = 0.73, RMSEP = 5.6 mg GAE/100 g). However, the TMA content could not be predicted. Finally, moving window analysis was conducted to determine the optimum wavelength ranges for predicting selected analytes. On average, this improved RMSECV values by an average of 18–20% compared to the corresponding full wavelength models, when using the same component selection method. The results confirm that infrared spectroscopy may be useful for the real-time quantitation and/or screening of key quality parameters in wheat, such as protein, TPC and antioxidant capacity.

Consumers of Australian cider are currently trending towards higher-quality cider products. As a result, boutique and craft cider breweries are expected to experience a period of growth over the next five years. Supporting this trend and subsequent growth is paramount to rebuilding the cider industry post-COVID-19. Many current practices and procedures, such as must clarification and biomass reduction in cider brewing, have been adapted from the beer and wine industry. While these practices are beneficial to the quality of cider and often promote the production of favourable volatile organic compounds (VOCs), the targeted enhancement of specific VOCs has not been achieved. This work investigates the specific enhancement of 2-phenylethanol (2-PE), which is known to improve the organoleptic properties of cider and provide potential health benefits through its antioxidant properties. The effect of three levels of biomass reduction (90%, 80%, and 0%) and five levels of L-phenylalanine (L-phe) saturation (0.5, 1.0, 1.5, 2.0, and 2.5 g L−1) for the enhanced production of 2-PE during cider fermentation were investigated. A high-performance liquid chromatography method was developed to accurately quantify the concentration of both 2-PE and L-phe, with a root-mean-square deviation (RSMD) of 0.41% and 1.60%, respectively. A significant increase in 2-PE production was achieved for all treatments, with 2-PE levels up to two orders of magnitude higher than respective controls. The highest 2-PE production was achieved by a moderate (80%) biomass reduction at a 2.5 g L−1 L-phe spike. Additionally, the VOC profile of several of the ciders was quantitively determined, and subsequent data underwent extensive chemometric analysis. Principle component analysis (PCA) showed that 2-PE and its derivatives (2-phenylethyl pivalate and phenylacetaldehyde) were correlated with the 80% biomass reduction treatment at the highest L-phe spike. Additionally, it was observed that several acids and alkanes were negatively correlated with the production of 2-PE and its derivatives. Additionally, hierarchical cluster analysis (HCA) showed clustering between the 80% and 90% biomass reduction treatments at several L-phe spike concentrations. However, the 0% biomass reduction treatments only showed similarity with other treatments with 0% biomass reduction. This work provides insight into the production of 2-PE during apple cider fermentation while building the foundation for more targeted biotechnological production of favourable compounds to improve cider quality.

Foxtail millet husk (FMH) is generally removed and discarded during the first step of millet processing. This study aimed to optimize a method using deep eutectic solvents (DESs) combined with ultrasonic-assisted extraction (UAE) to extract phenols from FMH and to identify the phenolic compositions and evaluate the biological activities. The optimized DES comprised L-lactic acid and glycol with a 1:2 molar ratio by taking the total flavonoid content (TFC) and total phenolic content (TPC) as targets. The extraction parameters were optimized to maximize TFC and TPC, using the following settings: liquid-to-solid ratio of 25 mL/g, DES with water content of 15%, extraction time of 41 min and temperature of 51 °C, and ultrasonic power at 304 W. The optimized UAE-DES, which produced significantly higher TPC, TFC, antioxidant activity, α-glucosidase, and acetylcholinesterase inhibitory activities compared to conventional solvent extraction. Through UPLC–MS, 12 phenolic compounds were identified, with 1-O-p-coumaroylglycerol, apigenin-C-pentosyl-C-hexoside, and 1-O-feruloyl-3-O-p-coumaroylglycerol being the main phenolic components. 1-O-feruloyl-3-O-p-coumaroylglycerol and 3,7-dimethylquercetin were identified first in foxtail millet. Our results indicated that FMH could be exploited by UAE-DES extraction as a useful source of naturally derived antioxidants, along with acetylcholinesterase and α-glucosidase inhibitory activities.

With growing consumer interest and demand for health‐benefiting functional foods such as faba beans, particularly evident in developed countries, commercial production of this crop is increasing. In concert with increased production levels comes an equally great need for the inexpensive rapid measurement of nutritional parameters for quality determining purposes. As an analytical tool, near‐infrared spectroscopy has been well explored for the quantification of proximate nutritional composition, such as protein, starch and oil contents in faba bean and faba bean‐derived products. Near‐infrared spectroscopy has also been demonstrated to have potential for the noninvasive prediction of low‐level micronutrients such as the total polyphenol content in faba bean and faba bean‐derived products, although further exploration in this area is required to provide a more acceptable model. In some instances, the authors may be inadvertently measuring micronutrient concentrations through a secondary correlation with certain macronutrients. It is particularly difficult to determine if this is the case if exacerbated by the lack of an independent validation test set in the paper in question. The associated technique of mid‐infrared spectroscopy shows particular promise for the rapid, noninvasive characterisation of structural components of faba bean, such as carbohydrates and proteins. Complementary applications of these two technologies are likely to yield a wealth of potential applications.

Faba bean (Vicia faba) is growing in popularity in Australia, partly due to its higher levels of health‐benefiting compounds compared to other grain crops. This study investigated infrared spectroscopy for predicting levels of bioactive compounds such as antioxidants and phenolics in faba bean flour. Calibration models were performed on 60 samples of faba bean, comprising 10 varieties grown across two field locations in 1 year. For model validation, an independent test set comprising the same varieties grown in a different year was utilised. Near‐infrared spectroscopy (NIRS) showed promise for the prediction of total phenolic content, with an R2pred of 0.66 and root mean square error of prediction (RMSEP) of 76 mg/100 g. Similarly, prediction of ferric reducing antioxidant power, a measure of antioxidant activity, gave an R2pred of 0.59 and RMSEP of 87 mg/100 g. Additionally, moving window optimisation was used to determine the most important wavelength region for the prediction of these analytes. Fourier transform infrared spectroscopy did not yield any suitable models for the analytes investigated. Although the NIRS models developed were not capable of exactly quantifying phenolic or antioxidant content, infrared spectroscopy appears useful for rapidly discriminating between samples containing high and low levels of phenolics or antioxidant compounds. With further refinement, this technique could potentially be applied for the quality assurance of phenolic content or antioxidant capacity in faba bean seeds.

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.