Spinach and amaranth are among the most consumed vegetables in
Eastern DRC where mothers are encouraged to consider them in their daily
diets as source of vitamins and minerals and to fight against infantile
malnutrition. During dry season prices are higher as compared to wet
season due to low productions; at the same time considerable production
losses are encountered during wet seasons due to poor conservation
facilities.
It’s in the regards that this study was conducted aiming to
reduce post-harvest losses by drying leaves; specifically in this study
two methods of drying leaves (sun and oven drying) will be assessed to
come up with the best; this study will help also diversify consumption
pattern in Eastern DRC. Organoleptic and physicochemical analyzes showed
that sun-dried vegetables have high values of moisture, iron, fat and
ash compared to oven dried vegetables; however, no significant
difference was observed between the two methods of drying as regards to
protein and calcium contents. Oven drying reduced the aroma, color,
taste and flavor of amaranths compared to the sun drying, but no
difference was found between the two methods for spinach. These results
imply that drying is a good method of preservation, making these
vegetables available during the period of scarcity. Oven and sun drying
are both good for spinach but precaution should be taken when applying
oven drying on amaranths.
Moisture content is a very essential
indicator for quality and storage stability of peanuts but its
measurement is tedious and time-consuming. This study ventured in a
rapid and non-destructive way of determining and predicting the moisture
content of peanut kernels utilizing latest technology. This study
generally aims to investigate the potential of hyper spectral imaging
technique in the near- infrared region (900nm – 1700nm) for determining
and predicting moisture content of peanut kernels.
Source: wikiwand.com
Using partial least
square regression (PLSR), spectral data from the peanut kernel
hyperspectral images were extracted to predict MC. The MC PLSR model
displayed good performance with determination coefficient of calibration
(R2c), cross- validation (R2cv) and prediction (R2p) of 0.9309, 0.9094
and 0.9316, respectively. In addition, root mean square error of
calibration (RMSEC), cross- validation (RMSECV) and prediction (RMSEP)
of 1.6978, 1.9571 and 1.8715, respectively. Optimization was done by
selecting wavelengths with the highest absolute weighted regression
coefficients resulting to 20 wavelengths identified. These wavelengths
were used to build the optimized regression model which resulted to
better model with R2c of 0.9357, R2cv of 0.9142 and R2p of 0.9445 as
well as RMSEC, RMSECV and RMSEP of 1.6822, 1.8316 and 1.9519,
respectively. The optimized model was applied to the peanut kernel
hyperspectral images in a pixel- wise manner obtaining peanut kernel
moisture content distribution map. Results show promising potential of
hyperspectral imaging system in the near- infrared region combined with
partial least square regression (PLSR) for rapid and non- destructive
prediction of moisture content of peanut kernels.