ASHS 2024 Conference

Microgreens are an emerging functional food that is sought after due to dense nutrient compositions as well as vibrant colors and textures. Seed contamination is one of the major food safety concerns as microgreens are consumed raw. Plant pathogenic diseases are also a concern as these can cause a reduction in the growth and quality of the crop. Seed sanitation methods should effectively reduce microbial load with minimal adverse effects on seed germination. The objective of this study was to investigate the effect of four seed sanitizing treatments on germination, shoot production and mineral nutrient concentrations of four microgreen species including chive (Allium schoenoprasum), shiso (Perilla frutescens var. crispa), scallion (Allium fistulosum) and dill (Anethum graveolens). Microgreen seeds were subject to four sanitizing treatments including: Tsunami 100 (400 ppm, 5 min), hydrogen peroxide (3%, 5 min), vinegar (1%, 15 min), and hot water (85°C, 10 sec). Seeds which were treated with deionized water for 10 min were considered to be the control. The microgreens were grown in a greenhouse and were planted into a peat-based substrate and a jute fiber mat in January 2024. Prior to greenhouse production, a germination test was conducted to investigate germination percentage of seeds for each species in response to the four sanitizing treatments or control. Microgreens were assessed for germination, shoot coverage, shoot height, fresh and dry shoot weight, and mineral nutrient concentrations. There was a significant interaction between microgreen species and the sanitizing treatment on fresh and dry shoot weight. The lowest fresh shoot weight for the three species chive, scallion and shiso was 938.2 g·m-2, 976 g·m-2, 907.8 g·m-2, respectively when treated with hot water, with the other three sanitizing treatments and control resulting in statistically similar fresh shoot weights. Dill microgreens showed little difference in fresh shoot weight among the five sanitizing treatments with values ranging from 506.4 g·m-2 in hot water to 868.2 g·m-2 in control. Sanitizing treatment and substrate type both had a significant effect on the shoot height of tested microgreens. Hot water treated microgreens produced the shortest shoots with a mean shoot height of 7.7 cm regardless of species or substrate type. The other four sanitizing treatments produced statistically similar shoot heights ranging from 8.01 cm with vinegar to 8.1 cm with Tsunami 100. The peat substrate increased overall shoot length in tested microgreens compared with jute fiber mats regardless of sanitizing treatment or species.

There are over 7,500 apple varieties grown worldwide, each with its own set of organoleptic characteristics such as flavor, texture, and appearance. However, no more than 150 varieties have been introduced broadly in the global market. Consumer preference for apples is influenced by a complex interplay of factors beyond taste. The sheer variety of apples available in the market creates a valuable opportunity for a digital app that can help consumers navigate and select the best options based on their quality trait preferences. This project, in cooperation with U.S. Apple Association, aims to ultimately develop a digital app that will recommend apple varieties based on consumer’s preferences regarding sweetness, sourness, juiciness, crispiness, flavor, color, texture, and nutrition content. Thus, we examined the relationship between various sensory and physico-chemical data to understand their significance in apple selection. Five cultivars of apples grown organically (‘Ambrosia’, ‘Cosmic Crisp’, ‘Gala’, “Sugar Bee®’, and ‘Sweet Tango’) were purchased from a retail store in Mid Atlantic area during the winter season. Thirty apples of each cultivar (n=30) were measured for volume, weight, height, width, specific density, surface area, circumference, fizziness and for skin color (L*, a*, b*, hue angle, chroma). Firmness parameters and acoustic texture parameters were also measured. Additionally, total juice content, soluble solid content (SSC), titratable acidity and pH were assessed. Consumer panels (n=45: female=22, male=23) were conducted to evaluate traits including sweetness, sourness, flavor, texture/mouth feel, and overall eating quality (OEQ) using a five-point scale. Consumers displayed a stronger preference for ‘Cosmic Crisp’ and ‘Sugar Bee®’ varieties compared to ‘Gala’ and ‘Ambrosia’. This preference is driven by significantly higher consumer ratings for firmness, sweetness, and sourness of ‘Cosmic Crisp’ and ‘Sugar Bee®’. As highlighted in previous studies, correlations between consumer-rated sensory scores and their corresponding instrumental measurements were low. Furthermore, OEQ showed a stronger correlation with sensory ratings (r=0.54-0.84 ) than instrumental measurements (r=0.02 to 0.49). This suggests that consumer evaluation is critical, and may be a more reliable indicator, for the development of a digital app, compared to instrumental measurements. An accompanying consumer survey (n=30) indicated that crispness is a key factor considered by consumers when choosing apples. This project provided valuable insights and potential issues when developing a user-friendly app for consumers. It identified the dominant factors influencing apple selection and showed methods to cross-validate sensory ratings with corresponding instrumental measurements.

Visual quality is an important factor for consumer purchasing decisions of fresh-cut lettuce. Consumer behavior towards produce quality has been studied via traditional human evaluations. For sensory studies, quality evaluations are commonly done by trained human panels and consumer panels. This study was to investigate the possibility of replacing human evaluation with a machine based approach, using image capturing and analysis, and determine whether efficiency of produce quality analysis can be enhanced. Three types of data were collected: (a) consumers’(n=200) evaluation of lettuce on the picture, (b) instrumental analysis of samples (package head-space gas composition (O2, CO2) and electrolyte leakage), (c) image analysis of lettuce on the pictures. For image analysis, ImagePro’s smart segment tool was used to classify the lettuce samples into five regions: adult leaf, baby leaf, rib, rib degradation, and leaf degradation. This was used to find and calculate L*a*b, hue angle, chroma values, area, and relative area of these regions. Lettuce samples consisted of four cultivars (Green Forest, King Henry, Parris Island Cos, PI 491224). Samples were measured on day 7, 10, and 13 of storage. To predict browning score, data sets (b) and (c) were fed into a regression algorithm. The scores assigned by trained panels served as the target variables. The results showed a strong correlation between consumer’s browning score on the pictures and predicted scores generated by the regression model (r=0.74). Interestingly, removing the instrumental data set (b) did not worsen the model’s performance. The model achieved an R2 of 0.92 and RASE of 8.90 when using trained data sets (a) and (c), and an R2 of 0.91 and RASE of 8.53 when using trained data set (c) only. While a correlation coefficient of 0.74 indicates a promising relationship between image analysis and human evaluation of browning score, it’s not sufficient to definitively replace human evaluation. Further studies with larger datasets and exploration of more advanced machine learning models could lead to a more robust statistical model.