Peanut Protein Quality Evaluation via Near-Infrared Spectroscopy
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Solution Overview
Problem
The lack of an effective method to evaluate the quality of peanut raw materials suitable for protein processing leads to decreased product quality and increased costs due to the use of mixed peanut varieties, which have varying protein quality, resulting in an unmet market demand for vegetable protein in China.
Innovation Solution
A method using supervised principal component regression analysis to determine the quality of peanut raw materials by analyzing fruit shape score, total protein content, leucine content, arginine content, and the mass percentage of a specific subunit, and applying near-infrared reflectance spectroscopy and Sodium dodecyl sulfate-polyacrylamide gel electrophoresis to establish a model for evaluating peanut protein powder quality, categorizing samples as suitable, substantially suitable, or not suitable for protein processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mixed peanut varieties are used in actual production, then the cost is reduced, but the product quality is decreased
Solution Approach 1:
The patent segments peanut varieties based on their suitability for protein processing by establishing an evaluation system that classifies varieties into different quality categories. This segmentation allows producers to select appropriate varieties for specific applications, resolving the contradiction between using mixed varieties for cost reasons and maintaining product quality.
Solution Approach 2:
The patent changes the evaluation parameters from general agricultural traits to specific protein quality parameters including protein content, amino acid composition (leucine, arginine), and conarachin content. This parameter transformation enables precise identification of varieties suitable for protein processing, allowing cost-effective selection without compromising quality.
2Adaptability or versatility
If the relationship between raw material variety and protein quality is not clear, then mixed varieties can be used, but the application in food processing is restricted
Solution Approach 1:
The patent establishes a feedback mechanism through the evaluation system that provides information about the protein quality characteristics of different peanut varieties. This feedback enables informed decision-making regarding variety selection for specific food processing applications, eliminating the restrictions caused by unclear variety-quality relationships.
Solution Approach 2:
The patent replaces traditional empirical variety selection with a scientific evaluation system based on near-infrared spectroscopy and supervised principal component regression. This substitution transforms the selection process from guesswork to precision science, enabling appropriate variety selection for food processing applications.
3Manufacturing precision
If comprehensive quality evaluation is performed, then product quality can be improved, but the analysis complexity increases
Solution Approach 1:
The patent introduces near-infrared spectroscopy as an intermediary tool that provides comprehensive quality information without requiring complex laboratory analysis. This intermediary technology bridges the gap between simple visual inspection and complex chemical analysis, enabling efficient quality evaluation.
Solution Approach 2:
The patent transforms multiple quality parameters (protein content, amino acid composition, conarachin content) into a unified evaluation model using supervised principal component regression. This parameter transformation simplifies the analysis by reducing dimensionality while preserving the essential quality information needed for variety selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method simplifies the analysis, improves product quality by identifying high-quality peanut varieties for specialized applications like ham sausage and peanut milk, and provides a standard for breeding and planting specialized varieties, enhancing the agricultural processing industry.
Implementation Method 1
determining the total protein content, leucine content and arginine content are the mass percentage of protein using near-infrared reflectance spectroscopy
Implementation Method 2
determining the mass percentage of subunit with molecular weight of 23.5 kDa to total protein using Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and densitometric analysis
Data Source
AI summary
A method of determining and evaluating quality of peanut raw material suitable for processing protein. The method includes the following step: determining fruit shape score, total protein content, leucine content, arginine content, conarachin I content and the mass percentage of the subunit with molecular weight of 23.5 kDa to total protein in the peanut sample to be tested; putting the determined values into formula (1) to obtain the protein powder quality of the peanut sample. The disclosure reduces the analysis step. The disclosure establishes the model of evaluating raw material quality for peanut protein processing, and the peanut protein powder quality can be determined by 6 peanut quality characteristics. The determination of indexes in the model can be predicted by the near infrared analyzer. Through the near infrared analysis of peanut kernel, the indexes in the model can be simultaneously predicted without any damage to the peanut kernel.