Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics

Munawar, Agus Arip; Hörsten, Dieter von; Wegener, Jens Karl; Pawelzik, Elke; Mörlein, Daniel

doi:10.1016/j.eaef.2015.12.004

Cited by 89 publications

(59 citation statements)

References 27 publications

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“…For example, previous work in our group predicted rapid and non-destructive analysis of mango quality attributes using NIRS and chemometrics [15]. NIRS makes use of the fact that near infrared (NIR) radiation in the range of 780–2500 nm predominantly interacts with hydrogen bonds—e.g., O–H, C–H, N–H, S–H.…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy

Adnan

Hörsten

Pawelzik

et al. 2017

Foods

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Moisture content (MC) is one of the most important quality parameters of green coffee beans. Therefore, its fast and reliable measurement is necessary. This study evaluated the feasibility of near infrared (NIR) spectroscopy and chemometrics for rapid and non-destructive prediction of MC in intact green coffee beans of both Coffea arabica (Arabica) and Coffea canephora (Robusta) species. Diffuse reflectance (log 1/R) spectra of intact beans were acquired using a bench top Fourier transform NIR instrument. MC was determined gravimetrically according to The International Organization for Standardization (ISO) 6673. Samples were split into subsets for calibration (n = 64) and independent validation (n = 44). A three-component partial least squares regression (PLSR) model using raw NIR spectra yielded a root mean square error of prediction (RMSEP) of 0.80% MC; a four component PLSR model using scatter corrected spectra yielded a RMSEP of 0.57% MC. A simplified PLS model using seven selected wavelengths (1155, 1212, 1340, 1409, 1724, 1908, and 2249 nm) yielded a similar accuracy (RMSEP: 0.77% MC) which opens the possibility of creating cheaper NIR instruments. In conclusion, NIR diffuse reflectance spectroscopy appears to be suitable for rapid and reliable MC prediction in intact green coffee; no separate model for Arabica and Robusta species is needed.

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Section: Introductionmentioning

confidence: 99%

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy

Adnan

Hörsten

Pawelzik

et al. 2017

Foods

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“…Previous research also shows NIRS works in measuring the TSS and TA of mango fruit and can be examined as one of the fast non-destructive techniques of an automatic sorting and grading system based on imaging technology where principal component regression (PCR) and partial least squares regression (PLSR) were used as calibration models to estimate SSC and TA at the same time by utilizing the treated NIR spectra (Munawar et al, 2013). The authors show that the greatest model for SSC prediction was accomplished when PLSR is applied in integration with standard normal variate (SNV) spectra while PLSR based on multiplicative scatter correction (MSC) spectra alignment model was discovered to be the greatest model in estimating TA.…”

Section: Near Infrared Spectroscopic (Nir)mentioning

confidence: 99%

Quality evaluation of mango using non-destructive approaches: A review

2020

JAFE

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Mango (Mangifera indica L.) is one of the most popular and nutritionally rich fruits. It is also acknowledged as the king of fruits in India. Quality attributes of mango fruit depends on its appearances such as size, shape, skin colour, flesh colour, flavour, sweetness, and aroma. Over the recent years, non-destructive techniques have been garnering the interest of researchers as potential technologies that can be used for quality assessment of fruits in a part of postharvest processing. The present patterns of non-destructive techniques are more efficient, inexpensive, yield faster and accurate results. This mini review paper focuses on some of the previous applications of non-destructive techniques in quality evaluation of mango, focusing specifically on the non-destructive technique based on quality parameters. The future trend of using non-destructive techniques for quality evaluation is also discussed in this review paper.

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“…Mango, which is known as king fruit, is one of the most popular fruits around the world due to its taste, appearance and high nutritional value [1]. According to [2], approximately 50% of all tropical fruits produced worldwide are mangos.…”

Section: Introductionmentioning

confidence: 99%

“…This analysis is sometimes complicated procedures, time-consuming and using of hazardous chemical solutions, from which may cause environmental pollutions. Therefore, unsuitable for the quality control of fresh agricultural products which requires real-time, rapid, on-line and non-destructive measurements [1].…”

Section: Introductionmentioning

confidence: 99%

The Application of Near Infrared Reflectance Spectroscopy as A Fast and Non-Destructive Method to Determine Inner Quality Parameters of Intact Mango

Munawar¹,

Meilina²,

Zulfahrizal³

2019

Proceedings of the Proceeding of the First International Graduate Conference (IGC) on Innovation, Creativity, Digital, &Amp; Te

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Generally speaking, to determine inner quality parameters such as soluble solids content (SSC) and vitamin C of intact mango or other fruits, several methods were already employed. Yet, most of them are based on chemical analysis and fruit extraction followed by other laboratory analysis. These methods often require complicated sample processing, longer time consuming and destructive. In the last three decades, the application of near infrared reflectance spectroscopy (NIRS) as a fast, robust and non-destructive method in agricultural industries is gaining more attentions. Thus, the main purpose of this present study is to apply the NIRS method in determining SSC and vitamin C of intact whole mango by developing prediction models. Near-infrared spectra data, in the form of diffuse reflectance spectrum, were acquired for a total of 53 mango samples. Spectra data were corrected and enhanced using mean normalization (MN), standard normal variate (SNV) and the combination of them (MN+SNV). Prediction models used to predict SSC and vitamin C of intact mangos were developed using partial least square regression (PLSR). The results showed that SSC and vitamin C can be predicted rapidly and simultaneously using NIRS method with maximum correlation coefficient (r) were 0.85 for SSC and 0.96 for vitamin C, with residual predictive deviation (RPD) index were 1.92 and 3.53 for SSC and vitamin C respectively. Based on obtained results, we may conclude that the NIRS method can be applied as an alternative fast and non-destructive method in determining quality parameters of intact mango.

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Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics

Cited by 89 publications

References 27 publications

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy

Quality evaluation of mango using non-destructive approaches: A review

The Application of Near Infrared Reflectance Spectroscopy as A Fast and Non-Destructive Method to Determine Inner Quality Parameters of Intact Mango

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