Authentication of Mango Varieties Using Near-Infrared Spectroscopy

Jha, Shyam Narayan; Jaiswal, Pranita; Narsaiah, K.; Kumar, Ramesh; Sharma, Rajiv; Gupta, Mansha; Bhardwaj, Rishi; Singh, Ashish Kumar

doi:10.1007/s40003-013-0068-4

Cited by 41 publications

(14 citation statements)

References 18 publications

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“…Their findings showed that overall, NNs perform best at the task. Neural networks were also employed in [17] to forecast monthly wholesale prices of two agricultural products. Ahumada and Cornejo [18] considered a similar problem, also taking into account possible cross-dependencies of different product prices.…”

Section: Introductionmentioning

confidence: 99%

Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

et al. 2021

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Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.

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Section: Introductionmentioning

confidence: 99%

Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

et al. 2021

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“…Spectroscopy is a non-destructive, inexpensive, rapid and reliable technique that has traditionally been used in food chemistry for qualitative and quantitative determination of different compounds in fruit samples, especially near infrared (NIR) spectroscopy (Nicolaï et al, 2007;Nicolaï et al, 2014;Magwaza et al, 2012;López et al, 2013). This technique has been utilised for the quantitative determination of soluble solids content (SSC), firmness, acidity, dry matter, chemical substances such as glucose, sucrose, citric acid, malic acid, starch or cellulose in different fruits (Schmilovitch et al, 2000;Nagle et al, 2010;Theanjumpol et al, 2013), and even to determine a maturity index (Jha et al, 2013), internal quality index (Cortés et al, 2016) or different appropriate indices for quality analysis (Attila and János, 2011).…”

Section: Introductionmentioning

confidence: 99%

Astringency assessment of persimmon by hyperspectral imaging

Munera

Besada

Blasco

et al. 2017

Postharvest Biology and Technology

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One of the current challenges of persimmon postharvest research is the development of non-destructive methods that allow determination of the internal properties of the fruit, such as maturity, flesh firmness and astringency. This study evaluates the usefulness of hyperspectral imaging in the 460-1020 nm range as a non-destructive tool to achieve these aims in Persimmon cv. 'Rojo Brillante' which is an astringent cultivar. Fruit were harvested at three different stages of commercial maturity and exposed to different treatments of CO 2 (95% CO 2-20 ºCfrom 0 to 24 h) in order to obtain fruit with different levels of astringency. Partial Least Square (PLS) based methods were used to classify persimmon fruits by maturity and to predict flesh firmness from the average spectrum of each fruit. The results showed a 97.9% rate of correct maturity classification and an R 2 P of 0.80 for firmness prediction with only five selected wavelengths. For astringency assessment, as our results showed that the soluble tannins that remain after CO 2 treatments are distributed irregularly inside the flesh, a model based on PLS was built using the spectrum of every pixel in the fruit. The model obtained an R 2 P of 0.91 which allowed the creation of the predicted distribution maps of the tannins in the flesh of the fruit, thereby pointing to hyperspectral systems as a promising technology to assess the effectiveness of the deastringency treatments that are usually applied before commercialising persimmons from astringent cultivars.

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“…More sophisticated approaches are based on neural networks methods, etc. [11]. Another approach is to use structural models [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

Drachal

2019

Sustainability

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In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

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Authentication of Mango Varieties Using Near-Infrared Spectroscopy

Cited by 41 publications

References 18 publications

Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Astringency assessment of persimmon by hyperspectral imaging

Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

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