Predictability of price of tea from sensory assessments and biochemical information using data‐mining techniques

Paul, Sanjoy K.

doi:10.1002/jsfa.3223

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…However, this study was unable to incorporate important variables such as technical changes and weather parameters into the estimated model. Paul (2008) used data mining techniques to predict tea price using a hedonic pricing approach based on sensory assessments and biochemical information. He used Wright et al's (2002) data and first explored the strength of the statistical association of biochemical parameters with sensory assessments and price, using ANOVA with methods of moments (MM) estimates.…”

Section: Price Modelling Related To the Tea Industrymentioning

confidence: 99%

“…Aponsu & Jayasundara 2012Aponsu & Jayasundara (2012) used polynomial regression for forecasting tea prices. The time index of tea prices were used as the explanatory variables Data mining Paul (2008) Fernando et al 2008Paul (2008) incorporated sensory assessment scores and biochemical parameters of tea in price modelling. Fernando et al used cluster analysis followed by time series analysis for forecasting tea prices.…”

Section: Polynomial Regressionmentioning

confidence: 99%

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The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries

Gunathilaka¹,

Tularam²

2016

JMS

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The global production and consumption of tea has been steadily increasing over the past decades. The tea industry has become a significant contributor to the economies of producing countries such as Kenya, Sri Lanka, India and China. Apart from its economic importance, the environmental and social importance of tea production has been recognised in the literature. However the industry is confronted by a number of challenges. These challenges include resource constraints, competition for land, unavailability of adequate labour, and climate change, as is noted in this article. All of the major tea producing countries have identified climate change as being a major challenge. Therefore, identification of the appropriate methods for modelling tea prices by incorporating a group of interacting time series variables such as price, production and weather variables to explain the dynamic relationships among these time series is important for producers. This article reviews and examines the approaches used to model tea price. In particular, various time series techniques are reviewed. The analysis clearly shows that quite a number of studies have been done on tea pricing. We found that VAR techniques have the ability to model the non-structural relationship of tea price alongside other time series variables which are endogenous and exogenous in nature. This paper also contributes to the existing literature by summarising the research undertaken on tea pricing to date.

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Section: Price Modelling Related To the Tea Industrymentioning

confidence: 99%

Section: Polynomial Regressionmentioning

confidence: 99%

The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries

Gunathilaka¹,

Tularam²

2016

JMS

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“…The international tea market is captured by large international concerns (Vickner and Davies, 2002). Together with a strong spatial concentration of trade, it favours the integration of regional tea markets, including price linkages between them (Gunathilaka and Tularam, 2016;Paul, 2008). A strong product and qualitative segmentation constitutes the factor that weakens linkages between regional markets (Tanui, Fang, Feng, Zhuang and Li, 2012).…”

Section: Introductionmentioning

confidence: 99%

Price Linkages Between Tea Markets: A Case Study for Colombo, Kolkata and Mombasa Auctions

Rembeza¹,

Radlińska²

2020

ERSJ

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Purpose: This paper aims to identify the price linkages among the three largest auction tea markets in Colombo (Sri Lanka), Kolkata (India), and Mombasa (Kenya). The hypothesis about the existence of long-term price linkages between markets was verified and the rate of short-term price reactions to exogenous price shocks on particular markets has been defined. Design/Methodology/Approach: Due to a diverse level of integration of particular variables, ARDL models were applied in the analyses. The bounds test was applied to test long-term price linkages. Short-term adjustment were described based on the restricted Error Correction Models. Findings: The obtained results indicated that market linkages were mainly long-term. Shortterm price linkages among markets were poor. The Colombo market had the largest number of price maker features, but the Kolkata market behaved more like a price taker. The article analyzes average prices of tea in the individual markets. Price differences do not include the tea class quality. Practical Implications: The results indicate that price information on other markets is primarily relevant for long-term decisions. In short-term decisions, information on tea prices on other markets is of relatively small importance. Originality/Value: The indication of central markets is important for market participants. It is difficult to determine a priori whether and if so which markets are the most important in the price setting process. The obtained results indicate that the Colombo market was the leading market in the long term. In the short term, price information on other markets was primarily relevant for the Kolkata market.

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Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis

Kovács

Dalmadi

Lukács³

et al. 2010

Journal of Chemometrics

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a Tea quality used to be traditionally evaluated by sensory panels. Their expertise is considerably better than that of the average consumers. However, there can be definite subjectivity in the judgment of sensory panels. A new direction is advanced sensory study that integrates sensory assessment with instrumental evaluation. The aim of this paper is to distinguish five Sri Lanka black teas from plantations of different geographical origins. From the tea samples, infusions were prepared with standard procedure. Three different test methods were applied: electronic nose, electronic tongue and sensory assessment. In the case of the electronic nose only the volatile components were evaluated. The Alpha Astree II potentiometric electronic tongue analyzed the liquid phase, while in the sensory profiling all perceptible attributes were tested. Data were analyzed with principle component analysis (PCA), linear discriminant analysis (LDA) and in the case of sensory analysis with one-way ANOVA. The partial least square regression (PLS) was used to estimate the sensory attributes. As a final result, we found that LDA maps show the quality differences of teas grown at different altitudes and geographical location of growing regions.

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Predictability of price of tea from sensory assessments and biochemical information using data‐mining techniques

Cited by 7 publications

References 22 publications

The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries

The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries

Price Linkages Between Tea Markets: A Case Study for Colombo, Kolkata and Mombasa Auctions

Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis

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