Method for evaluating coffee bean climate quality

Abstract

This invention belongs to the field of agricultural climate technology, and more particularly, relates to a method for assessing the climate quality of coffee beans. This method calculates the climate quality of coffee beans by correlating physical indicators at sampling points with meteorological data. By quantitatively studying the relationship between meteorological conditions during the coffee growing season and its physical properties, a coffee bean quality analysis and evaluation model and related indicator system are established. This approach can provide a scientific basis for adjusting the layout of different coffee growing areas, evaluating coffee quality, and determining the purchase price of green coffee beans.

Description

Technical Field

[0001] The invention belongs to the technical field of agricultural climate, and in particular relates to a method for evaluating the climate quality of coffee beans. Background Art

[0002] Climate conditions in agricultural production areas are a key factor influencing their quality. In recent years, meteorological departments across my country have been conducting climate quality assessments or certifications for agricultural products. By setting certification climate condition indicators and establishing certification models, they assess the impact of weather and climate on agricultural product quality during the production phase and comprehensively assess the climate quality of agricultural products. This certification process has been implemented in 16 provinces and autonomous regions, including Shaanxi, Yunnan, Ningxia Hui Autonomous Region, Sichuan, and Guizhou. This has increased the contribution of meteorological science and technology to the agricultural economy and opened up new pathways for converting scientific and technological productivity into economic benefits.

[0003] Coffee, along with cocoa and tea, is considered one of the world's three major beverages, ranking first in both production and consumption. Yunnan Province is my country's largest green coffee bean producing region. Located on a low-latitude plateau, Yunnan's unique geography and climate provide a unique and scarce resource for developing high-quality coffee. It is also a major producer of Arabica coffee worldwide. Therefore, selecting a rational coffee bean evaluation method provides a scientific basis for determining coffee bean purchase prices and adjusting the layout of coffee-growing areas, positively impacting the quality, efficiency, and income of Yunnan's coffee industry. Unlike other plant-based climate quality assessments, where the chemical content of agricultural products is the primary criterion for climate quality assessment, coffee, as a tea beverage, is primarily reflected in its taste. However, coffee taste is a complex variable, and existing literature has failed to identify significant correlations between the content and ratio of caffeine, total sugars, reducing sugars, ash, water-soluble extracts, and fat in coffee beans and coffee taste. On the other hand, data and simulated regression equations indicate that ash and water-soluble extracts in the chemical composition of green coffee beans are significantly correlated with temperature, while other chemical components are not significantly correlated with climate conditions.

[0004] Therefore, how to find the correlation between coffee bean quality and climatic conditions is a core issue that needs to be solved urgently in coffee evaluation. Summary of the Invention

[0005] In view of the problem that existing climate assessment methods are not suitable for coffee rating, the present invention proposes a method for assessing the climate quality of coffee beans.

[0006] The method for evaluating the climate quality of coffee beans of the present invention is characterized by correlating physical indicators of sampling points with meteorological data. The physical indicator authentication model is as follows:

[0007] Thousand-grain weight = 226.757 + 5.169*T6 - 16.064*T7 + 9.895*T8 - 4.272*T9 + 0.125*R11 +0.351*R 12 ;

[0008] ≥17# Elephant Beans and Spotted Beans Quantity = 116.774 + 7.254 * T6 - 27.608 * T7 + 24.275 * T8 - 8.385 * T9 + 0.195 * R 11 +0.368*R 12 ;

[0009] ≥17# normal bean quantity = 96.931 + 7.386 * T6 - 26.839 * T7 + 21.72 * T8 - 5.998 * T9 + 0.166 * R 11 +0.321*R 12 ;

[0010] Among them, Rx is the average precipitation in month x of the year, and Tx is the average temperature in month x of the year.

[0011] Then calculate the climate quality of coffee beans, the formula is:

[0012] Among them, Q is the comprehensive score of climate quality physical indicators, q is the historical ranking of coffee bean thousand-grain weight, s is the historical ranking of the number of ≥17# elephant beans and spotted beans, t is the historical ranking of the number of ≥17# normal beans, and n is the number of selected years;

[0013] When Q<1 / 3, the evaluation level is excellent;

[0014] When 1 / 3≤Q<2 / 3, the evaluation level is excellent;

[0015] When Q≥2 / 3, the evaluation level is general.

[0016] In practice, it's been found that high-altitude coffee beans are often known for their superior flavor. This is because high-altitude coffee beans tend to have a higher density and larger size. Cupping experiments have confirmed a correlation between coffee bean size, density, and quality. Therefore, when measuring coffee quality based on climate, coffee beans can be graded based on the screen system. The current unit of measure for coffee size is 1 / 64 inch. Therefore, a No. 17 screen is 17 / 64 inch, meaning coffee beans can pass through a screen with holes of 6.75 mm.

[0017] To avoid introducing duplicate variables, correlation analysis was conducted among coffee bean physical property test items to identify the key physical property indicators for green coffee bean climate quality certification. These indicators include, but are not limited to, 1000-bean weight, the number of 17# or larger elephant and speckled beans, the number of 17# or larger normal beans, the number of 17# or larger broken beans, the number of <17# or larger elephant and speckled beans, and the number of <17# or larger normal beans. To avoid data redundancy, these indicators were carefully selected based on green coffee bean quality certification standards. Specifically, 1000-bean weight, the number of 17# or larger elephant and speckled beans, and the number of 17# or larger normal beans were selected as evaluation indicators based on factors such as the degree of significant positive correlation and statistical difficulty.

[0018] Based on this, coffee bean 1000-kernel weight, the number of 17# or larger elephant beans and speckled beans, and the number of 17# or larger normal beans showed significant to extremely significant negative correlations with the monthly average temperature during the early to late fruit expansion phases. This suggests that lower monthly average temperatures from May to September are more conducive to increases in 1000-kernel weight, the number of 17# or larger elephant beans and speckled beans, and the number of 17# or larger normal beans, and are therefore more beneficial to coffee quality. Furthermore, coffee bean 1000-kernel weight, the number of 17# or larger elephant beans and speckled beans, and the number of 17# or larger normal beans showed significant to extremely significant positive correlations with monthly precipitation during the early fruit expansion phase (July) and the red fruit phase (November and December). This suggests that increased precipitation in July, November, and December is conducive to increases in 1000-kernel weight, the number of 17# or larger elephant beans and speckled beans, and the number of 17# or larger normal beans. It can be seen that the selection of the coffee bean thousand-grain weight, the number of ≥17# elephant beans and spotted beans, and the number of ≥17# normal beans is the optimal solution obtained by comprehensively considering the degree of significant positive correlation, statistical difficulty, monthly average temperature and monthly precipitation.

[0019] Further analysis of the correlation between coffee bean physical properties and monthly temperature and precipitation revealed that green coffee bean physical properties were primarily significantly or extremely significantly correlated with average temperature from May to September and precipitation from July and November to December. However, the average temperature and precipitation data for these months were relatively scarce, necessitating further simplification of the factors to improve the timeliness of the assessment. Through comprehensive research, researchers used the monthly average temperature and precipitation from March to December as independent variables. Taking into account the long coffee growing season, they also used meteorological factors with significant correlations as independent variables. Linear regressions were performed using the aforementioned physical properties as dependent variables, resulting in extremely significant regressions.

[0020] Finally, the physical analysis data of the sampling points and the meteorological data of each sampling point were used to establish a statistical and regression model of the physical indicators and chemical composition of the coffee beans through regression statistics. Finally, the meteorological data of the year was substituted into the regression model to calculate the physical indicators and chemical composition of the coffee beans, so as to evaluate the climate quality of the coffee beans.

[0021] By studying the quantitative relationship between meteorological conditions during the coffee growing period and the physical properties of coffee, the present invention establishes a coffee bean quality analysis and evaluation model and a related indicator system, which can provide a scientific basis for adjusting the layout of different coffee growing areas, evaluating coffee quality, and formulating the purchase price of coffee beans, which has positive significance for improving the quality, efficiency and income of the coffee industry. DETAILED DESCRIPTION

[0022] Example 1: The present invention is implemented in Lujiangba, Yunnan Province. The plantation area is 10,000 mu, with a slope of 0 to 15 degrees, and most plots are at an altitude of 1,000-1,400 meters. The annual average temperature in the area where the production base is located is 21.2°C, the annual average precipitation is 778 mm, and the annual average sunshine hours are 2,318 hours. The overall climate characteristics are: hot and dry, sufficient sunlight, and frost-free all year round. With the presence of irrigation facilities, the growth and development requirements of coffee in all growth stages can be met. The meteorological data used in this certification are the daily temperature and precipitation data from March 1, 2007 to December 31, 2017 provided by the Lujiangba regional automatic weather station. The station is 875 meters above sea level, with a vertical height difference of about 300 meters from the base, and a straight-line distance of about 17 kilometers from the plantation.

[0023] According to the regression model, the thousand-grain weight of Lujiangba coffee in 2017 was 108.77 grams, 2.29 grams higher than the average, ranking 6th since 2007. The number of elephant beans and spotted beans ≥17# was 26.60%, 5.26% higher than the average, ranking 6th since 2007. The number of normal beans ≥17# was 24.74%, 8.44% higher than the average, ranking 5th since 2007.

[0024] The climate quality grade is calculated according to the formula: n=11, q=6, s=6, t=5. The final calculated value is Q=0.527 (1 / 3≤Q<2 / 3). Therefore, the climate quality grade of the green coffee beans produced in this area is certified as excellent.

Claims

1. A method for evaluating the climate quality of coffee beans, characterized in that By correlating the physical indicators of the sampling points with meteorological data, the physical indicator authentication model is as follows: Thousand-grain weight = 226.757 + 5.169 * T6 - 16.064 * T7 + 9.895 * T8 - 4.272 * T9 + 0.125 * R 11 +0.351*R 12 ; ≥17# Elephant Beans and Spotted Beans Quantity=116.774+7.254*T6-27.608*T7+24.275*T8-8.385*T9+0.195*R 11 +0.368*R 12 ; ≥17# normal bean quantity=96.931+7.386*T6-26.839*T7+21.72*T8-5.998*T9+0.166*R 11 +0.321*R 12 ; Among them, Rx is the average precipitation in month x of the year, and Tx is the average temperature in month x of the year; Then calculate the climate quality of coffee beans, the formula is: Among them, Q is the comprehensive score of climate quality physical indicators, q is the historical ranking of coffee bean thousand-grain weight, s is the historical ranking of the number of ≥17# elephant beans and spotted beans, t is the historical ranking of the number of ≥17# normal beans, and n is the number of selected years; When Q<1 / 3, the evaluation level is excellent; When 1 / 3≤Q<2 / 3, the evaluation level is excellent; When Q≥2 / 3, the evaluation level is general.