Light manipulation as a route to enhancement of antioxidant properties in red amaranth and red lettuce.

With the growing global population and climate change, achieving food security is a pressing challenge. Vertical farming has the potential to support local food production and security. As a Total Controlled Environment Agriculture (TCEA) system, vertical farming employs LED lighting which offers opportunities to modulate light spectrum and intensity, and thus can be used to influence plant growth and phytochemical composition, including antioxidants beneficial for human health. In this study, we investigated the effect of four red-to-blue light ratios of LEDs (R:B 1, 2.5, 5 and 9) on the growth and antioxidant components in red amaranth microgreens and red lettuce. Plant growth, total phenols, betalains, anthocyanins, vitamin C and antioxidant capacity (ferric reducing antioxidant power assay) were evaluated. A higher proportion of red light resulted in biometric responses, i.e., stem elongation in red amaranth and longer leaves in red lettuce, while the increase in the blue light fraction led to the upregulation of antioxidative components, especially total phenols, betalains (in red amaranth) and anthocyanins (in red lettuce). The antioxidant capacity of both crops was strongly positively correlated with the levels of these phytochemicals. Optimizing the red-to-blue ratio in LED lighting could be effective in promoting antioxidant-rich crops with potential health benefits for consumers.

High pH thresholding of beef with VNIR hyperspectral imaging.

Initial quality grading of meat is generally carried out using invasive and occasionally destructive sampling for the purposes of pH testing. Precise pH and thresholds exist to allow the classification of different statuses of meat, e.g. for detection of dry, firm, and dark (DFD) (when dealing with cattle and sheep), or pale, soft exudative meat (when dealing with pork). This paper illustrates that threshold detection for pH level in beef with different freshness levels (fresh, fresh frozen-thawed, matured, and matured frozen-thawed). Use of support vector machine (SVM) analysis allowed for the classification of beef samples with a pH above 5.9, and below 5.6, with an accuracy of 91% and 99% respectively. Biochemical and physical conditions of the meat concerning the pH are discussed.

Classification of organic beef freshness using VNIR hyperspectral imaging.

Consumer trust in the food industry is heavily reliant upon accurate labelling of meat products. As such, methods, which can verify whether meat is correctly labelled are of great value to producers, retailers, and consumers. This paper illustrates two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements. The results show successful classification based upon CIELAB between 1) fresh and frozen-thawed (CCR=0.93), and 2) fresh and matured (CCR=0.92). With successful classification between matured and matured frozen-thawed beef using the entire spectral range (CCR=1.00). The performance of reduced spectral models is also investigated. Overall it was found that CIELAB co-ordinates can be used for successful classification for all comparisons except between matured and matured frozen-thawed. Biochemical and physical changes of the meat are thoroughly discussed for each condition.