The main applications of near-infrared spectrometer in grain analysis

1. Quantitative analysis of grain composition

• Moisture content determinationNear infrared spectroscopy is sensitive to O-H bond vibrations and can accurately determine the moisture content in grains such as wheat, corn, and rice, with an error range typically within ± 0.2%

• Protein content analysisFeature absorption based on N-H bonds for rapid detection of protein content in crops such as wheat and soybeans

• Fat content determinationSpecially suitable for fat content analysis of oil crops such as rapeseed and soybeans

• Starch to amylose ratioThe evaluation of edible quality of crops such as rice and corn is of great significance

• Cellulose and ash contentEvaluate the nutritional value and processing characteristics of grains

2. Grain quality evaluation

• Food quality predictionBy establishing PLS or ANN models, predict rice taste value, wheat flour baking quality, etc

• Mold and toxin detectionIdentify fungal contamination and toxin (such as aflatoxin) content in grains

• Germination potential assessmentPredicting the vitality and germination rate of grain seeds

• Determination of degree of agingDistinguish between new and old grains to ensure the quality of grain storage

3. Grain variety identification and origin tracing

• Based on principal component analysis (PCA) or discriminant analysis (DA) algorithms, achieve rapid identification of different varieties of wheat, rice, etc

• Combining chemometric methods to trace the geographical origin of grains and ensure product authenticity

4. Process monitoring

• Online monitoring of protein, moisture and other indicators during flour processing

• Quality control of brewing industrial raw materials (barley, sorghum)

• Formula optimization and quality control in feed production

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5. Conclusion

Near infrared spectroscopy technology has become a modern method for grain analysisimportantTools play an important role in various aspects of grain procurement, storage, processing, and trade. With the improvement of instrument performance and the advancement of data analysis methods, this technology will demonstrate greater application potential in the fields of smart agriculture and food quality and safety monitoring. Future research should focus on model transferability, standardized operating procedures, and low-cost equipment development to promote the wider application of this technology in the grain industry chain.