Spent coffee grounds (SCGs) are abundant byproducts generated during coffee processing that are unsuitable for storage and subsequent value-added utilization owing to their high moisture content and water activity (a_w). This study investigated the effects of different infrared power levels (800, 900, and 1000 W) on drying kinetics, product quality, and energy efficiency to determine the preferred drying parameters for SCGs. The initial moisture content and a_w of SCGs were 63.56% (wet basis) and 0.95, respectively. To enhance mechanistic understanding, the drying data were fitted to four mathematical models, with the Midilli and Page models providing the best fit (R² > 0.99). Drying experiments were conducted under a sample thickness of 0.7 cm and a loading of 500 g, with a final moisture content of <10% as the drying endpoint. The results showed that as infrared power increased, drying time decreased from 30 to 24 min and the drying rate significantly increased from 10.32 to 12.77 g H₂O/min (p < 0.05). The drying process was mainly characterized by a falling-rate period, with the effective moisture diffusivity ranging from 0.97 to 1.15 × 10⁻⁸ m²/s and increasing with rising power, indicating that internal moisture diffusion was the dominant drying mechanism. The final a_w of each treatment group was ≤0.60, indicating good storage stability. Color analysis showed that the color differences in treatments at higher power levels (900 W and 1000 W) were significantly lower than those at lower ones (p < 0.05). While the specific energy consumption (SEC) showed a marginal decrease from 5.80 to 5.68 kWh/kg at higher power, a comprehensive evaluation of drying efficiency, quality characteristics, and energy consumption indicated that 1000 W was the preferred infrared drying power under the conditions employed in this study. These results confirm that infrared drying is an efficient stabilization method with strong potential for rapid stabilization of food processing byproducts.

Pork floss is a common dried meat product in Asia. The endpoint of the baking process is traditionally determined by subjective human experts and indirect temperature measurements, which can often result in unstandardized production. Current colorimeters are unavailable for onsite measurement due to limitations associated with contact measurement and environmental temperature. Instead of the abovementioned human experts and tabletop colorimeters, a handheld colorimeter was built based on the expertise of human specialists and utilizing a tabletop colorimeter and other optical steps. First, the selected samples were used to determine the upper and lower limits distinguishing light, medium, and heavy baking levels by using a tabletop colorimeter. Second, independent light sources and spectrometers were utilized to choose the characteristic and reference wavelengths at 450 and 830 nm, separately. Third, the handheld colorimeter, instead of human expert observation, was designed with functions such as distance sensing and Internet of Things capabilities. The baked index was derived from the calibration reflection and established statistical models. Here, the calibration reflection was defined by the normalized intensity at 450 nm relative to 830 nm, and statistical models were founded from the determined samples of upper and lower limits at 95–700 mm. The developed handheld colorimeter demonstrated high agreement rates of 96.84% and 93.86% in separate comparisons with tabletop colorimeters and human experts, respectively. This work indicated the accurate and stable recognition of samples within two limits and overall. Field validation confirmed the performance of remote, economic, and onsite recognition against environmental temperature and noise.

Soybean residue (Okara), a major by-product of soybean product processing, is highly susceptible to spoilage due to its high moisture content (approximately 78% w.b.), which creates both environmental and resource-related challenges. This study aimed to develop energy-efficient drying technologies and value-added application models to improve its storage stability. The effects of heat pump drying, microwave drying, and two-stage drying on the drying kinetics, energy consumption, and product quality of okara were systematically compared. The experimental results indicated that Heat Pump Drying (HPD) at 65 °C required a prolonged drying time of 360 min. In contrast, Microwave Drying (MWD) at 2.0 W/g significantly accelerated the process, achieving the shortest drying time of 50 min (an 86.1% reduction) and lowering the specific energy consumption (SEC) to 2.2 kWh/kg (a 42.1% energy saving compared to HPD). Meanwhile, the HPD–MWD two-stage drying process offered a balanced alternative, requiring 190 min and reducing thermal risk while maintaining high efficiency. The dried okara powder contained a total dietary fiber content of 47.78%, while its water activity was maintained below 0.60. Dynamic Dew Point Isotherm (DDI) analysis confirmed a critical water activity (a_wc) of 0.66, with mathematical modeling predicting a shelf life of up to 389 days under barrier packaging conditions. In value-added application experiments, muffins containing 10% okara powder achieved sensory scores above 6 on a 9-point scale and demonstrated significantly better flavor acceptability (p = 0.0093). In summary, this study established an efficient drying and value-added application approach for okara, providing a feasible strategy for the circular use and sustainable utilization of agricultural by-products.

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage’s curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400–700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades (‘Over light’, ‘Light’, ‘Standard’, ‘Dark’, and ‘Over dark’) with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (n = 150) achieved high sorting accuracies of 95.3–97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry.