研究目的
To evaluate and compare the nutritive value of two varieties of lentils (green and red) by determining their chemical components using NIRS technology.
研究成果
Both green and red lentils have good nutritional quality with slight differences: green lentils have higher ash, crude protein, and total fibers, while red lentils have higher crude fat and total carbohydrates, leading to a slightly higher metabolizable energy. Further studies are recommended to assess amino acids and unsaturated fatty acids for potential use in bakery products.
研究不足
The study is preliminary and focuses only on major chemical components; it does not assess amino acids or unsaturated fatty acids. The NIRS method relies on calibration with known samples, which may introduce errors if not perfectly matched. Sample size and variety are limited to two types of lentils, and results may not be generalizable to all lentil varieties.
1：Experimental Design and Method Selection:
The study used near infrared reflectance spectroscopy (NIRS) to non-destructively and rapidly evaluate the chemical composition of lentil samples. This method was chosen for its efficiency and cost-effectiveness compared to traditional chemical methods.
2：Sample Selection and Data Sources:
Two varieties of lentils (red and green) were purchased from specialized stores. Samples were mechanically chopped with a laboratory mill to avoid alteration of parameters and stored at 4°C before analysis.
3：List of Experimental Equipment and Materials:
Equipment included a FOSS NIR 5000 device for reflectance measurements in the wavelength region 1100-2500 nm, a scan cup dry for sample preparation, and a computer for data analysis. Materials included polypropylene bags for storage.
4：Experimental Procedures and Operational Workflow:
Samples were chopped, placed in the scan cup to form an even layer, sealed, and scanned using the NIRS device. Measurements were done in triplicate, and mean values were calculated.
5：Data Analysis Methods:
The NIRS software performed calibration before scanning and estimated nutrient profiles based on statistical models and reference values.
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