Method of determining and evaluating quality of peanut raw material suitable for protein processing

Abstract

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.

Claims

1. A method of determining quality of peanut raw material suitable for protein processing, comprising: with a controller, determining each of a fruit shape score, a total protein content, a leucine content, an arginine content, a conarachin I content, and a mass percentage of a subunit with a molecular weight of 23.5 kDa to total protein in a peanut sample to be tested, wherein determining the fruit shape score comprises obtaining image data from a camera, and with the controller, analyzing the image data, the analyzing including categorizing a fruit shape of the peanut sample, and wherein when the fruit shape of the peanut sample is categorized as hockey stick-shaped, the fruit shape score is 1; when the fruit shape of the peanut sample is categorized as hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample is categorized as string of beads-shaped, the fruit shape score is 3; when the fruit shape of the peanut sample is categorized as an ordinary shape, the fruit shape score is 4; when the fruit shape of the peanut sample is categorized as wasp waist-shaped, the fruit shape score is 5; when the fruit shape of the peanut sample is categorized as calabash-shaped, the fruit shape score is 6; when the fruit shape of the peanut sample is categorized as cocoon-shaped, the fruit shape score is 7; and when the fruit shape of the peanut sample is categorized as axe-shaped, the fruit shape score is 8, wherein the total protein content, leucine content, and arginine content are mass percentages of protein, leucine, and arginine to the peanut sample respectively, wherein the conarachin I content is a mass percentage of conarachin I to total protein, wherein the method further comprises, with the controller, putting the above determined values into the following equation to obtain a protein powder quality of the peanut sample: peanut protein powder quality=1.6560.046fruit shape score+0.007total protein content0.91leucine content+0.005arginine content0.013conarachin I content0.017mass percentage of the subunit with the molecular weight of 23.5 kDa to total protein, wherein the method further comprises, with the controller, displaying the peanut protein powder quality and/or transmitting the peanut protein powder quality to another electronic system, and adjusting an actuator based on the determined quality.

2. A control system for determining quality of peanut raw material suitable for protein processing, comprising: a controller for receiving signals from sensors and sending control signals to actuators, wherein the control system is programmed with computer readable data representing instructions executable to: determine each of a fruit shape score, a total protein content, a leucine content, an arginine content, a conarachin I content, and a mass percentage of a subunit with a molecular weight of 23.5 kDa to total protein in a peanut sample to be tested, wherein determining the fruit shape score comprises obtaining image data from a camera; and analyze the image data, including categorizing a fruit shape of the peanut sample, wherein when the fruit shape of the peanut sample is categorized as hockey stick-shaped, the fruit shape score is 1; when the fruit shape of the peanut sample is categorized as hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample is categorized as string of beads-shaped, the fruit shape score is 3; when the fruit shape of the peanut sample is categorized as an ordinary shape, the fruit shape score is 4; when the fruit shape of the peanut sample is categorized as wasp waist-shaped, the fruit shape score is 5; when the fruit shape of the peanut sample is categorized as calabash-shaped, the fruit shape score is 6; when the fruit shape of the peanut sample is categorized as cocoon-shaped, the fruit shape score is 7; and when the fruit shape of the peanut sample is categorized as axe-shaped, the fruit shape score is 8, wherein the total protein content, leucine content, and arginine content are mass percentages of protein, leucine, and arginine to the peanut sample, respectively, wherein the conarachin I content is a mass percentage of conarachin I to total protein, wherein the instructions further comprise instructions executable to put the above determined values into the following equation to obtain a protein powder quality of the peanut sample: peanut protein powder quality=1.6560.046fruit shape score+0.007total protein content0.91leucine content+0.005arginine content0.013conarachin I content0.017mass percentage of the subunit with the molecular weight of 23.5 kDa to total protein, and instructions executable to take actions responsive to the peanut protein powder quality, wherein the actuators include a sorting machine, and wherein the controller is configured to send a signal to the sorting machine, the signal based on the peanut protein powder quality.

3. A method of determining quality of peanut raw material suitable for protein processing, comprising: with a controller, determining each of a fruit shape score, a total protein content, a leucine content, an arginine content, a conarachin I content, and a mass percentage of a subunit with a molecular weight of 23.5 kDa to total protein in a peanut sample to be tested, wherein determining the fruit shape score comprises obtaining image data from a camera, and with the controller, analyzing the image data, the analyzing including categorizing a fruit shape of the peanut sample, and wherein when the fruit shape of the peanut sample is categorized as hockey stick-shaped, the fruit shape score is 1; when the fruit shape of the peanut sample is categorized as hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample is categorized as string of beads-shaped, the fruit shape score is 3; when the fruit shape of the peanut sample is categorized as an ordinary shape, the fruit shape score is 4; when the fruit shape of the peanut sample is categorized as wasp waist-shaped, the fruit shape score is 5; when the fruit shape of the peanut sample is categorized as calabash-shaped, the fruit shape score is 6; when the fruit shape of the peanut sample is categorized as cocoon-shaped, the fruit shape score is 7; and when the fruit shape of the peanut sample is categorized as axe-shaped, the fruit shape score is 8, wherein the total protein content, leucine content, and arginine content are mass percentages of protein, leucine, and arginine to the peanut sample respectively, wherein the conarachin I content is a mass percentage of conarachin I to total protein, wherein the method further comprises, with the controller, putting the above determined values into the following equation to obtain a protein powder quality of the peanut sample: peanut protein powder quality=1.6560.046fruit shape score+0.007total protein content0.91leucine content+0.005arginine content0.013conarachin I content0.017mass percentage of the subunit with the molecular weight of 23.5 kDa to total protein, wherein the method further comprises, with the controller, displaying the peanut protein powder quality and/or transmitting the peanut protein powder quality to another electronic system, and wherein the method further comprises, with the controller, taking actions responsive to the peanut protein powder quality, the actions including adjusting transportation of the peanut sample via actuation of one or more actuators without human intervention.

4. The method of claim 3, wherein the one or more actuators include a transporter device, a sorting machine, and/or an inspection machine.

5. A method of evaluating quality of peanut raw material suitable for protein processing, comprising: determining protein powder quality of a peanut sample to be tested according to the method of claim 1, and with the controller, classifying the peanut sample according to the following criteria 1) to 3): 1) if a calculated value of the peanut protein powder quality 76, then the peanut sample is suitable for protein powder processing; 2) if the calculated value of the peanut protein powder quality is 67.5-76, then the peanut sample is substantially suitable for protein powder processing; 3) if the calculated value of the peanut protein powder quality 67.5, the peanut sample is not suitable for protein powder processing, wherein: if the peanut sample is classified as suitable for protein powder processing, with the controller, sending a signal to an actuator to direct the peanut sample along a first path; if the peanut sample is classified as substantially suitable for protein powder processing, with the controller, sending a signal to the actuator to direct the peanut sample along a second path; and if the peanut sample is classified as not suitable for protein powder processing, with the controller, sending a signal to the actuator to direct the peanut sample along a third path.

6. The method of claim 5, wherein the actuator is a component configured to direct the peanut sample along a processing path in processing equipment.

7. The method of claim 6, wherein the component is a sorting machine vane.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows the fitting chart of the original values and calculated values of the protein powder quality of 20 peanut samples in Example 2.

(2) FIG. 2 shows a control system programmed with instructions to perform the methods and operations described herein.

DETAILED DESCRIPTION OF EXAMPLES

(3) The experimental methods used in the following Examples are all conventional methods, unless otherwise specified.

(4) The materials, reagents and the like used in the following Examples are all commercially available, unless otherwise specified.

Example 1. Establishment of Model for Determining Quality of Peanut Suitable for Protein Processing

(5) (1) Determination of Peanut Quality

(6) Taking peanut samples harvested in 2016 as a standard and 100 samples (in line with the normal distribution rule of peanut population, as shown in Table 1);

(7) TABLE-US-00001 TABLE 1 100 peanut varieties No Variety name 1 Zhonghua 8 2 Shanhua 7 3 Silihong 4 Luhua 11 5 Bianhua 3 6 Haihua 1 7 Shuangji 2 8 Shanhua 9 9 Fenghua 5 10 Yueyou 14 11 Yueyou 45 12 Yueyou 86 13 Minhua 9 14 Guihua 771 15 Zhanhua 82 16 Shanyou 250 17 Longhua 243 18 Heyou 11 19 Zhenzhuhong 20 White peanut 21 Fenghua 1 22 Fenghua 3 23 Fenghua 4 24 Xuhua 5 25 Yuanhua 8 26 Xuhua 13 27 Huayu 20 28 Huayu 23 29 Huayu 28 30 Huayu 31 31 Baisha 1016 32 Wucai peanut 33 Black peanut 34 034-256-1 35 Ji 9814 36 Yuhua 15 37 Yuhua 9326 38 Yuhua 9327 39 Kainong 30 40 Kainong 37 41 Yuanza 9102 42 Zhongnong 108 43 Quanhua 551 44 Honghua 1 45 Qinglan 8 46 Huayu 8 47 Luhua 14 48 Xuhua 15 49 Huayu 16 50 Zhonghua 4 51 Zhonghua 15 52 Haiyu 6 53 Lufeng 2 54 Hongguan 55 Yuanza 9307 56 Zhengnong 7 57 Fenghua 6 58 Luhua 9 59 Luhua 15 60 Xianghua 509-77 61 Hua 17 62 Jihua 4 63 Jihua 5 64 Jihua 8 65 Jihua 9 66 Jihua 10 67 Jihua 12 68 XY-1 69 Huayu 16 70 Huayu 19 71 Huayu 25 72 Huayu 33 73 Huayu 36 74 Puhua 28 75 Puhua 9519 76 Puzhenhua 1 77 Yuhua 9502 78 JS024 79 JS0537 80 Ningtai 9922 81 Taihua 4 82 Zhonghua 16 83 XY-1 84 Hongsha 1 85 Huayu 36 86 Huayu 39 87 Huayu 51 88 Huayu 52 89 Luhua 12 90 SH08 91 SH09 92 Gaoyou 1 93 Gaoyou 3 94 Hetian 95 Huayu 22 96 Jiaokusi 97 Wujiaokusi 98 Xinjiang 1 99 Xinjiang 3 100 Xinjiang 4

(8) The 45 indexes of each variety, such as sensory quality, physicochemical and nutritional quality and processing quality, are determined; wherein each index and the determination methods and standards thereof are as follows:

(9) peanut physical traits: fruit shape: when the fruit shape of the peanut sample is hockey stick-shaped, the fruit shape score is 1; when the fruit shape of the peanut sample is hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample is a string of beads-shaped, the fruit shape score is 3; when the fruit shape of the peanut sample is ordinary shape, the fruit shape score is 4; when the fruit shape of the peanut sample is wasp waist-shaped, the fruit shape score is 5; when the fruit shape of the peanut sample is calabash-shaped, the fruit shape score is 6; when the fruit shape of the peanut sample is cocoon-shaped, the fruit shape score is 7; when the fruit shape of the peanut sample is axe-shaped, the fruit shape score is 8; seed shape: see Study on the Main Traits of Peanut Varieties-Differences among Trait Performances and Types [J] (Luan Wenqi, 1986, Luan Wenqi, Feng Haisheng and Wang Jingshan, China Seed Industry, 1986, 23-7.); red skin: see Wan Shubo (Wan Shubo, 2008; Wan Shubo, Peanut Quality [M]. Beijing: China Agricultural Science and Technology Press, 2008); weight of 100 fruits: randomly taking 100 peanut fruits and weighing, then repeating 3 times for the average; weight of 100 kernels: randomly taking 100 peanut kernels and weighing, then repeating 3 times for the average;

(10) analysis of physiochemical and nutritional quality of peanut: moisture content: GB/T 5009, 3-2003; fat content: GB/T 5009, 6-2003; protein content: GB/T 5009, 5-2003; ash content: GB/T 5009, 4-2003; crude fiber content: GB/T 5515-2008; amino acid content: GB/T 5009.124-2003; sugar content; protein subunit content: SDS-PAGE gel electrophoresis is used to determine the composition and content of protein subunits in these peanut varieties, the separation gel concentration is 13% and pH is 8.8; the concentration gel concentration is 5% and pH is 6.8; electrode buffer is 0.025 M Tris-HCl, 0.192 M glycine and 0.1% SDS, and the pH is 8.3; the gel after electrophoresis is photographed with the gel imaging system, US FlourChem V 2.0, and the relative content of each component is analyzed using Alpha Ease FC software; among the above indexes, the protein subunit content refers to the mass percentage of protein subunit to the protein, and the rest of the indexes refers to the mass percentage to peanut;

(11) peanut processing quality analysis: protein extraction rate: the protein is extracted with the method of alkali-solution and acid-precipitation, and protein extraction rate=extracted protein mass/protein mass in peanuts100%; kernel rate: weights of kernels obtained from 100 g of peanut fruits/100 g100%;

(12) The variation range, mean value, standard deviation, variation coefficient, upper quartile, median and lower quartile of the basic data of selected 61 peanut varieties are analyzed. The results are shown in Table 2.

(13) TABLE-US-00002 TABLE 2 Quality Characteristics of Peanut Variety Variation Mean Standard Variation Upper Lower Factor range value deviation coefficient quartile Median quartile Fruit shape 1.00- 5.07 1.86 36.78 4.00 5.00 7.00 8.00 Red skin 1.00- 5.47 1.46 26.62 5.00 6.00 6.00 9.00 Seed shape 1.00- 2.40 1.79 74.54 1.00 1.00 5.00 5.00 Weight of 100 fruits 114.80- 183.0 43.42 23.72 149.50 183.50 213.80 285.00 7 Weight of 100 38.6-120 72.16 18.64 25.83 57.75 71.90 85.20 kernels Water content 3.71- 5.47 0.95 17.43 4.71 5.36 6.18 7.41 Crude fat 42.11- 51.22 3.40 6.63 49.29 51.24 53.59 58.59 Protein 21.42- 25.79 2.06 7.97 24.37 25.78 27.09 31.4 Total sugar 2.87- 7.30 2.56 35.08 5.02 7.03 9.59 12.59 Ash 2.19- 2.57 0.20 7.86 2.45 2.56 2.65 3.46 Crude fiber 1.5-6.9 2.53 0.82 32.28 2.10 2.50 2.80 Total amino acids 19.08- 23.99 2.26 9.44 21.89 24.19 25.11 30.69 Aspartic acid 2.22- 2.78 0.30 10.62 2.55 2.77 2.94 3.61 Threonine 0.40- 0.63 0.08 12.76 0.59 0.62 0.68 0.87 Serine 0.81- 1.10 0.11 10.45 1.04 1.10 1.16 1.42 Glutamate 2.05- 4.36 0.66 15.14 4.00 4.36 4.71 6.12 Proline 0.79- 1.21 0.21 17.62 1.0 1.16 1.36 1.70 Glycine 1.11- 1.36 0.13 9.20 1.28 1.36 1.43 1.66 Alanine 0.63- 0.90 0.17 18.58 0.77 0.91 1.00 1.38 Cystine 0.35- 0.62 0.23 37.94 0.45 0.55 0. 00 1.14 Valine 0.90- 1.13 0. 14 10.76 1.06 1.10 1.18 1.51 Methionine 0.09- 0.32 0.14 43.56 0.22 0.28 0.38 0.71 Isoleucine 0.71- 0.92 0.13 14.56 0.82 0.91 1.02 1.26 Leucine 1.28- 1.61 0.19 11.76 1.46 1.64 1.74 1.99 Tyrosine 0.46- 0.91 0.25 27.09 0.79 0.89 1.05 1.51 Phenylalanine 0.80- 1.39 0.18 12.83 1.28 1.41 1.51 1.81 Lysine 0.77- 0.97 0.09 8.80 0.90 0.97 1.03 1.15 Histidine 0.47- 0.63 0.12 18.22 0.55 0.60 0.67 0.92 Tryptophan 0.16- 0.24 0.05 19.22 0.22 0.24 0.26 0.42 Arginine 2.38- 2.92 0.30 10.37 2.73 2.91 3.09 3.78 Globulin 47.60- 59.68 6.11 10.24 54.45 58.70 64.77 73.02 Conarachin 26.99- 40.34 6.15 15.25 35.23 41.30 45.75 52.20 Conarachin I 9.68- 21.98 5.91 26.89 17.04 24.10 26.15 31.00 Conarachin II 14.70- 18.36 1.89 10.28 16.90 18.05 19.84 21.90 Globulin/Conarachin 0.91- 1.54 0.43 27.70 1.19 1.42 1.84 2.71 40.5 kDa 7.73- 10.36 1.60 15.42 9.32 10.14 11.40 14.50 37.5 kDa 10.50- 14.89 1.59 10.71 13.81 15.00 16.01 17.90 35.5 kDa 0-19.20 10.21 5.22 51.17 10.85 12.30 13.17 23.5 kDa 18.70- 24.23 3.84 15.87 20.90 23.00 26.90 32.64 18 kDa 2.12- 7.22 2.82 39.08 4.48 7.76 9.40 11.40 17 kDa 1.74- 8.41 2.83 33.72 6.23 9.20 10.50 12.50 15.5 kDa 2.33- 6.35 2.18 34.29 4.93 6.40 7.75 11.90 Protein extraction 59.51- 74.00 5.78 7.81 70.71 74.63 77.57 rate (%) 88.97 Kernel rate (%) 50.31- 69.93 5.94 8.50 66.06 70.02 74.08 79.94

(14) The variation coefficient is a statistical magnitude for measuring variation degree in a set of data. The variation coefficients of fat content, protein content, ash content, total amino acid content, glycine content, lysine content, protein extraction rate, kernel rate is <10% (6.63%, 7.97%, 7.86%, 9.44%, 9.20%, 8.80%, 7.81%, 8.50%, respectively), and low variation coefficients indicate small dispersion degrees. The variation coefficients of other indexes are relatively high, indicating relatively large differences existing in many qualities of different peanut varieties. With comparison of mean values and median, it can be found that except that the differences of seed shape and 35.5 kDa content is 58.33% and 20.50% respectively, the median of other qualities are close to their mean values, indicating outliers in these data is relatively few.

(15) (2) Determination of Peanut Protein Powder Quality

(16) Peanut protein powder quality includes: 10 indexes including fat content, crude fiber content, protein purity, ash content, hardness, elasticity, cohesion, water-holding capacity, oil-holding capacity and solubility, and they are calculated according to the following formula:
peanut protein powder quality=0.012fat content+0.090crude fiber content0.210protein purity+0.300ash content+0.1618hardness+0.3781elasticity+1.1573cohesion0.035water-holding capacity-0.320oil-holding capacity0.469solubility;

(17) fat content: GB/T 5009, 6-2003; crude fiber content: GB/T 5515-2008; all the above content refers to the mass percentage to peanut; protein purity: protein content/protein mass in protein powder100%; ash content GB/T 5009, 4-2003;

(18) water-holding capacity of protein: the determination uses the improved method of Beuchat L. R. (1977); the specific method is as follows:

(19) 1) accurately taking 1.000 g samples into a centrifuge tube and weighing; then adding 10 mL of distilled water to prepare 10% protein solution;

(20) 2) vibrating with vertical movements for 5 min such that all proteins are homogeneously dispersed; after standing at room temperature for 30 min, centrifuging at 3000 r/min for 20 min; carefully removing the supernatant and then weighing; the computational formula of water binding capability is as follows:

(21) $\mathrm{WBC}=\frac{W_2-W_1}{W_0};$

(22) wherein W.sub.0 is protein mass, g; W.sub.1 is the weight of the centrifuge tube, g; W.sub.2 is the weight of centrifuge tube after removing the supernatant, g;

(23) oil-holding capacity of protein: the improved method of Chakraborty P. (1986) is used; the specific method is as follows:

(24) accurately taking 1.000 g samples into a centrifuge tube and weighing; then adding 5 mL of soybean salad oil;

(25) vibrating with vortical movements for 5 min such that all proteins are homogeneously dispersed; after standing at room temperature for 30 min, centrifuging at 3000 r/min for 20 min; removing the supernatant salad oil and then weighing. The computational formula of fat binding capability is as follows:

(26) $\mathrm{FBC}=\frac{W_2-W_1}{W_0};$

(27) wherein each symbol is the same as the WBC formula;

(28) protein solubility: accurately weighing 1.000 g peanut protein (N) (protein purity is P) into a triangular flask and adding 40 mL of distilled water before shaking homogeneously; placing in 30 C. constant temperature water bath oscillator, vibrating at 150 r/min for 120 min, and then transferring the mixture to 50 mL (V1) volumetric flask before adding water to constant volumes; then standing for 5 min and taking the supernatant to centrifugate at 1500 r/min for 10 min, and then filtering the supernatant with quantitative filter paper; taking 15 mL (V2) into a digestion tube and concentrating in an oven at 150 C. for 120 min; then using Kjeldahl Nitrogen to determine the concentration (C, mg/mL). The computational formula of nitrogen solubility index is as follows:

(29) $\mathrm{NSI}=\frac{C\frac{V_1}{V_2}}{PN}100$

(30) TABLE-US-00003 TABLE 3 Quality Characteristics of 100 Peanut Protein Powders Data Variation Mean Standard Upper Lower Variation becoming range value deviation quartile Median quartile coefficient/% poor/% Ash 0.74-2.35 1.35 0.32 1.11 1.32 1.50 24.09 2.22 Crude fat 0.54-8.84 2.52 1.60 1.35 2.26 3.26 63.56 10.32 Crude fiber 0.00-1.02 0.28 0.22 0.14 0.23 0.34 77.57 17.86 Protein 85.64-94.81 90.57 1.90 89.44 90.51 91.89 2.10 0.07 purity Water- 0.74-1.38 1.07 0.18 0.94 1.08 1.23 16.90 0.93 holding capacity Oil- 1.04-1.71 1.34 0.12 1.26 1.33 1.42 8.89 0.75 holding capacity Hardness 0.37-4.26 1.60 0.86 0.91 1.60 2.09 53.37 0.00 Elasticity 0.53-0.97 0.75 0.11 0.68 0.75 0.82 15.09 0.00 Cohesion 0.28-0.59 0.39 0.06 0.35 0.39 0.43 15.04 0.00 Solubility 57.63-93.44 79.33 9.43 81.91 80.12 76.76 11.89 0.97

(31) (3) Establishing the Model of Evaluating Quality of Peanut Protein Powder with the Supervised Principal Component Analysis

(32) The supervised principal component analysis does not use all the regression independent variables to establish model, and it only uses the independent variables which have strong correlation with the corresponding variables. According to the correlation coefficient between the corresponding variable and each independent variable, the regression independent variable set is screened. The independent variables with the correlation coefficient exceeding a certain threshold are screened out, and then the principal component regression analysis on the partial regression independent variables which are newly screened out are carried out. The supervised principal component regression analysis is carried out to establish the model for the first 80 varieties in selected 100 varieties.

(33) 1. Independent Variable Analysis (Peanut Quality)

(34) The outliers of peanut quality (for No. 2, 17 and 23 peanut varieties) are removed using Boxplot method, so the remaining 38 varieties are analyzed.

(35) 2. Screening Evaluation Indexes of Peanut Quality

(36) According to concept of regression analysis, the regression coefficient significance analysis is carried out for single index of peanut quality and protein quality and it is found that 6 indexes have significant correlation at 0.01 level with the protein powder quality.

(37) TABLE-US-00004 TABLE 4 Regression Significance Index Table of Peanut Quality and Protein Powder Quality No. Index P value 1 Fruit shape 0.011 2 Crude protein 0.010 3 Crude fiber 0.018 4 Leucine 0.009 5 Arginine 0.005 6 Conarachin I 0.003 7 23.5 kDa 0.001

(38) 3. Principal Component Analysis

(39) Principal component analysis and dimensionality reduction on the 6 screened indexes are carried out, as shown in Table 5.

(40) TABLE-US-00005 TABLE 5 The Eigenvalues of Correlation Matrix Variance contribution Cumulative variance ratio/% contribution ratio/% 1 45.09 45.09 2 21.52 66.61 3 15.66 82.27 4 7.81 90.07 5 6.21 96.28

(41) Through the principal component analysis, it is found that the cumulative contribution rate of the first 3 principal components is 82.27%. Therefore, the first 3 principal components can express the original principal component information, and the original 6 indexes are transformed into 3 new indexes, playing the role of dimensionality reduction. SAS is used to output the eigenvectors (not listed here) of the first 3 principal components, and the linear relationship between the first 3 principal components and each index. The relational expression between each principal component and each independent variable is used to calculate each principal component score. Each principal component score of each evaluation object can be obtained by putting the normalized data into each relational expression, as shown in Table 6.

(42) TABLE-US-00006 TABLE 6 Each Principal Component Score No. Principal component 1 Principal component 2 Principal component 3 1 0.33513 0.04893 1.82966 2 0.90626 0.24662 2.74372 3 2.38043 0.35297 0.22615 4 1.05145 1.72192 1.70074 5 1.828 0.3175 0.2592 6 197085 0.31743 0.52289 7 0.6035 1.97114 1.00505 8 2.28598 1.67866 0.51693 9 0.64892 1.5006 0.14137 10 1.07462 0.92645 0.10074 11 0.3344 1.20402 0.12344 12 0.26419 0.95198 0.65105 13 0.0007 0.25002 1.04761 14 0.00738 1.08647 1.57252 15 0.322 1.37846 0.56167 16 0.12353 0.19127 0.99232 17 0.81486 1.80951 0.27637 18 0.39675 0.62271 1.02459 19 0.865 0.65647 1.33418 20 0.12587 0.77792 1.23228 21 1.28274 0.77738 0.38345 22 0.56524 0.25049 0.04527 23 0.84427 1.07593 0.3657 24 1.56148 0.56384 0.10501 25 0.2038 0.02885 0.16002 26 0.85122 0.57793 0.82137 27 0.2339 1.69847 1.52744 28 0.73286 0.8857 0.25135 29 1.39272 0.25378 0.31745 30 0.02028 0.15063 1.20141 31 1.29556 0.42034 0.11298 32 0.87203 0.60832 0.66893 33 0.85097 1.10865 0.37452 34 0.9059 1.65452 1.20532 35 0.37831 0.0617 1.74358 36 0.34961 1.18449 0.30082 37 0.58504 1.17134 0.23583 38 0.10674 0.26919 1.27448

(43) 4. Establishment of Regression Equation

(44) The regression analysis showed that the regression coefficients of principal components 2 and 3 and protein powder quality are significant at 0.05 level (as shown in Table 7). Therefore, the relationship between principal components 2 and 3 and the protein powder quality is established, and the relationship between each index and protein powder quality is further established. The result is shown in the formula (1).

(45) TABLE-US-00007 TABLE 7 Regression Coefficient Significance Variable Coefficient P value Intercept 0.994 0.01 PC1 0.1232 10.sup.6 0.737 PC2 0.008 0.035 PC3 0.50 0.045

Example 2. Determination of Peanut Protein Powder Quality

(46) The remaining 20 peanut varieties of Example 1 are subjected to protein powder quality determination.

(47) The fruit shape score, crude protein content, leucine content, arginine content, conarachin I and the mass percentage of the subunit with molecular weight of 23.5 kDa of the 20 peanut varieties are put into formula (1) to calculate the protein powder quality of the 20 varieties. The comparison between the model prediction value and the chemical measurement value of the peanut protein powder quality is shown in Table 8; and the regression analysis on the calculated result of the model and the determined protein powder quality is carried out, and the correlation coefficient is 0.815, as shown in FIG. 1.

(48) TABLE-US-00008 TABLE 8 Comparison of Model Prediction Value and Chemical Measure Value of Peanut Protein Powder Quality Calculated Peanut variety Original value value Absolute error Relative error/% 1 0.956366 0.982096 0.02573 2.690405768 2 0.878048 0.694 0.184048 20.96101604 3 1.138497 1.105532 0.032965 2.895482134 4 0.975065 0.931867 0.043198 4.430309178 5 0.950299 0.903721 0.046578 4.901405643 6 1.01263 1.156137 0.143507 14.17170433 7 0.816033 0.925182 0.109149 13.37556937 8 1.041211 0.938321 0.10289 9.881798557 9 0.68934 0.752932 0.063592 9.225008988 10 0.887131 0.89405 0.006919 0.77990647 11 0.900956 0.906679 0.005723 0.635244245 12 1.01584 1.093221 0.077381 7.617481622 13 0.922367 1.130256 0.207889 22.53865526 14 0.818072 0.845398 0.027326 3.340253518 15 0.965279 1.00908 0.043801 4.537611922 16 0.72536 0.740208 0.014848 2.046986087 17 1.038059 1.011532 0.026527 2.555429174 18 0.71568 0.648147 0.067533 9.436219609 19 0.67934 0.621112 0.058228 8.57119107 20 0.918874 1.063923 0.145049 15.78546674

Example 3. Establishment of Method of Evaluating Peanut Quality Suitable for Protein Processing

(49) By using K-means clustering analysis and the actual situation, the protein powder quality of 100 peanut varieties is classified into two groups, suitable and substantially suitable (Table 9).

(50) TABLE-US-00009 TABLE 9 Suitability Analysis of Protein Powder Processing Number Class- Stand- of ification ard sample Sample name Suitable 1.08 14 Luhua 11, Shuangji 2, Bianhua 3, Fenghua 1, Kainong 30, Feng Hua 3, Shanhua 7, Minhua 9, Zhanhua 82, Yuhua 15, Haihua 1, Honghua 1, Ji 9814, and Yueyou 14 Substantially 0.85- 34 034-256-1, Shanhua 9, Huayu suitable 1.08 16, Luhua 9, Kainong 37, Lufeng 2, Luhua 14, Zhonghua 8, Guihua 771, Shanyou 250, Xuhua 13, Qinglan 8, Silihong, Fenghua 5, Huayu 23, Zhengong 7, Zhongnong 108, Huayu 8, Yuanhua 8, Hongguan, hua 17, Zhenzhuhong, Huayu 20, Haiyu 6, Yuhua 9327, Longhua 243, Heyou 11, black peanut, Zhonghua 15, Yueyou 86, white peanut, Yuhua 9326, Quanhua 551, and Xuhua 5

(51) The remaining 52 varieties are not suitable.

(52) According to the regression coefficient, the weight of each index is determined. By using K-means clustering analysis and the actual situation, each evaluation index is classified into Class I, Class II and Class III, and the weight value of each index is used as Class I score, and so on.

(53) TABLE-US-00010 TABLE 10 Weight of Each Index in Formula (1) No Index Weight 1 Fruit shape 12 2 Crude protein 16 3 Leucine 12 4 Arginine 17 5 Conarachin I 21 6 23.5 kD 22

(54) The 6 quality indexes of peanut are analyzed by K-means clustering, and each index is classified into 3 categories: Class I (suitable), Class II (substantially suitable) and Class III (not suitable). The weight of each index is used as the highest score, i.e., Class I, and so on, and the corresponding scores are given to each level of index, as shown in Table 11.

(55) TABLE-US-00011 TABLE 11 Scores of Each Index at Each Level Index Class I Class II Class III Fruit shape Category value 3 3-6.54 >6.54 Score 12 8 4 Crude protein+ Category value 27.13 24.27-27.13 <24.27 Score 16 12 8 Leucine Category value 1.39 1.39-1.66 >1.66 Score 12 8 4 Arginine+ Category value 3.70 3.00-3.70 <3.30 Score 17 13 9 Conarachin I+ Category value 29.04 23.63-29.04 <23.63 Score 21 16 11 23.5 kDa Category value 20.80 20.80-23.72 >23.72 Score 22 17 12

(56) The sum of each trait index score is used as the final score of each peanut variety. According to the K-means clustering analysis formula, the final score of each variety is classified into 3 categories: Class I (suitable), Class II (substantially suitable) and Class III (not suitable), as shown in Table 12.

(57) TABLE-US-00012 TABLE 12 Classification of Peanut Variety Based on K-means Clustering Analysis Method Classifi- Number of cation Standard sample Sample name Suitable 76 11 Luhua 11, Shuangji 2, Bianhua 3, Fenghua 1, Honghua 1, Luhua 14, Kainong 30, Zhanhua 82, Minhua 9, Yueyou 14, Yuhua 15, and Ji 9814 Substantially 67.5-76 35 Fenghua 3, Haiyu 6, Shanhua 9, suitable Shanyou 250, Zhenzhuhong, Yuanhua 8, 034-256-1, Feng Hua 3, Lufeng 2, Shuangji 2, Hongguan, Fenghua 6, Haihua 1, Silihong, Quanhua 551, Huayu16, white peanuts, Fenghua 1, Zhongnong 108, Qinglan 8, Yuhua 9326, Yuhua 9327, Guihua 771, Xuhua 13, Yueyou 86, Heyou 11, Kainong 37, Huayu 31, Hua 17, Huayu 20, Zhonghua 8, and Luhua 15

(58) The remaining 54 varieties are not suitable.

(59) The results of Table 11 are compared with the results in Table 12, and the matching degree is: suitable varieties account for 92.6%, substantially suitable varieties account for 84.7% and not suitable varieties account for 71.2%, indicating that the evaluation results are relatively good and suitable for being used as evaluation standard of peanut quality suitable for processing protein powder.

(60) Referring to FIG. 2, a control system 70 including controller 71 is shown. Controller 71 may receive various signals from sensors 72, and send control signals to various actuators 73. The various sensors may include, for example, a camera. The various actuators may include a sorting machine, for example. The controller 71 may send a signal to the sorting machine. Controller 71 may be a microcomputer, including a microprocessor unit, input/output ports, an electronic storage medium for executable programs and calibration values. Controller 71 may be programmed with computer readable data representing instructions executable to perform the methods described herein as well as variants that are anticipated but not specifically listed. The control system may be part of a processing plant and be coupled with transporter devices, sorting machines, inspection machines, and the like. The system may be programmed to carry out the quality determination and take actions responsive thereto, such as adjusting transportation of the inspected element via actuation of one or more actuators via the controller without human intervention, and thereby provide efficient processing of the image data of the element to be inspected and thus fast and accurate quality determination.

(61) In one example, a method for automatically processing protein power may include, via a processor having instructions stored in memory and in communication with sensors and actuators, the instructions for determining quality of peanut raw material suitable for protein processing, including 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 a peanut sample to be tested from one or more sensed parameters sensed by a sensor contacting or sensing the raw material; wherein when the fruit shape of the peanut sample to be tested is determined to be hockey stick-shaped sensed via a camera for example and determined via the instructions analysing image data from the camera, the fruit shape score is 1; when the fruit shape of the peanut sample to be tested is determined to be hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample to be tested is determined to be a string of beads-shaped, the fruit shape score is 3; when the fruit shape of the peanut sample to be tested is ordinary shape, the fruit shape score is 4; when the fruit shape of the peanut sample to be tested is wasp waist-shaped, the fruit shape score is 5; when the fruit shape of the peanut sample to be tested is calabash-shaped, the fruit shape score is 6; when the fruit shape of the peanut sample to be tested is cocoon-shaped, the fruit shape score is 7; when the fruit shape of the peanut sample to be tested is axe-shaped, the fruit shape score is 8. The shape determination via the image processing instructions in the processor may be based on taking camera image data and first determining an outline of the peanut compared with a contrasting background of a known color (e.g., via thresholding each pixel value) and then processing the detected edge and categorizing the resulting shape into only one and exactly one of the options listed above so that the shape is uniquely identified. The instructions may further include that the total protein content, leucine content and arginine content are the mass percentage of protein, leucine and arginine to the peanut sample to be tested respectively, where these parameters may be determined by near-infrared reflectance spectroscopy sensors.

(62) The instructions may further include that the conarachin I content is the mass percentage of conarachin I to total protein, which may be determined by Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and densitometric analysis or related sensors. The instructions may put the above determined values into formula (1) stored in memory to obtain the protein powder quality of the peanut sample to be tested and display the determined quality and/or transmit the determined quality to another electronic system, and may include adjusting an actuator such as a sorting machine based on the determined quality. Further, in one example, peanut protein powder quality=1.6560.046fruit shape score+0.007total protein content0.91leucine content+0.005arginine content0.013conarachin I content0.017mass percentage of subunit with molecular weight of 23.5 kDa to total protein, which may be stored in the memory. Instruction may also include classifying the peanut sample to be tested according to the following criteria 1) to 3):

(63) 1) if the calculated value of the peanut protein powder quality 76, then the peanut sample to be tested is suitable for protein powder processing and directed along a first path via an actuator for the processing;

(64) 2) if the calculated value of the peanut protein powder quality is 67.5-76, then the peanut sample to be tested is substantially suitable for protein powder processing but directed along a second path via the actuator; and

(65) 3) if the calculated value of the peanut protein powder quality 67.5, the peanut sample to be tested is not suitable for protein powder processing and directed along a third path via the actuator, which may be a sorting machine vane or other component to direct the raw material along a processing path in processing equipment.