Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer

Marques, Emanuel José Nascimento; Freitas, Sérgio Tonetto de; Pimentel, Maria Fernanda; Pasquini, Célio

doi:10.1016/j.foodchem.2015.11.080

Cited by 142 publications

(71 citation statements)

References 28 publications

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“…Walsh and Subedi (2016) using a Nirvana spectrometer in field reported a DM model performance of R CV 0.94% and RMS CV 0.68%. Lower RMSE P values (0.67 and 0.51%) were observed by Marques et al (2016) using a MicroNIR 1700 in 'Tommy Atkins' mango.…”

Section: Dmmentioning

confidence: 71%

“…Comparing the performance of the SSC prediction model with other studies, the RMSE CV value of 1.39% was quite similar to the SEP of 1.2% reported by Rungpichayapichet et al (2016) who studied 'Nam Dokmai' and 'Si Thong' mango varieties using a VIS/NIR spectrometer (HandySpec Campo 1000, tec5AG, Oberursel, Germany) on the range of 700-1100 nm (SWNIR). On the other hand, lower SEP values were reported by Saranwong et al (2003) using a 'FT20' spectrometer (Fantec, Japan) on ripe 'Carabao' mangoes (0.40%; R = 0.96 ; 900-1000 nm) and by Marques et al (2016) using a MicroNIR 1700 (VIAVI, Santa Rosa, CA, USA) on the wavelength range of 950-1650 nm (RMSE P of 0.92°Brix) for 'Tommy Atkins' mango.…”

Section: Sscmentioning

confidence: 89%

“…Rungpichayapichet et al (2016) studying the maturity and postharvest quality of 'Nam Dokmai' and 'Si Thong' mangoes reported a standard error of prediction (SEP) for SSC of 1.2% and a correlation coefficient (R 2 ) of 0.90; a R 2 = 0.82, SEP = 4.22 N for firmness; R 2 = 0.74, SEP = 0.38% for TA; and R 2 = 0.80, SEP = 0.80 for ripening index. Marques et al (2016) using a portable MicroNIR 1700 reported a root mean square error of prediction (RMSEP) of 0.92°Brix, 0.51%, 0.17%, and 12.2 N for SSC, DM, TA, and firmness, respectively. More recently, Walsh and Subedi (2016) developed models for in field DM content estimation with R 2 of 0.94 and root mean square error of cross validation (RMSECV) of 0.68%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer

Neto

Assis²,

Casagrande

et al. 2017

Postharvest Biology and Technology

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Palmer' mango cultivar is a late season variety which is greatly accepted by European consumers. However, it is common to get reports of fruit quality problems, mainly due to maturity. Thus, the objective of this study was to develop calibration models for soluble solids content (SSC) and dry matter (DM) of 'Palmer' mangoes using portable (VIS-NIR) spectrometer. Interactance spectra were obtained with a portable F-750 spectrometer in the wavelength range of 306-1140 nm, 8 nm spectrum resolution, and 4 scans averaged per spectra. Spectra were used to develop SSC and DM models using partial least square regression (PLSR) with full cross validation. The best SSC calibration model was developed using spectra pre-processed with standard normal variate (SNV), first derivative of Savitzky-Golay and window of 699-999 nm. It was observed a RMSE CV of 1.39%, with a R CV 2 of 0.87, and RPD of 2.77. Better results were observed for the DM calibration model which was built with raw spectra using the window of 699-981 nm (RMSE CV of 8.81 g kg −1 , R CV 2 of 0.84, and RPD of 2.51). Poor calibration models were obtained for firmness. The results indicated that portable VIS-NIR spectrometer can be used as a non-destructive technique to assess SSC and DM content for 'Palmer' mangoes. It is necessary to incorporate more sources of variation, to reduce RMSE values and improve robustness, especially for fruit SSC and DM prediction.

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Section: Dmmentioning

confidence: 71%

Section: Sscmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer

Neto

Assis²,

Casagrande

et al. 2017

Postharvest Biology and Technology

View full text Add to dashboard Cite

show abstract

“…Coefficient of determination and root mean square errors of prediction (RMSEP) values were, respectively: 0.92 and 0.55 Brix for SS, 0.67 and 0.51% for DM, 0.50 and 0.17% citric acid for TA, 0.72 and 12.2 N for PF [8]. And the results show that it is feasible to determine quality parameters in the'Tommy Atkins'mango using the new NIR spectrometer.…”

Section: Objective Analysis Based On Near Infrared Technologymentioning

confidence: 88%

Nondestructive Determination of Fruit Based on Sensory and Near-Infrared Technology

Liu¹,

Tan²

2017

Proceedings of the 2016 2nd International Conference on Economics, Management Engineering and Education Technology (ICEMEET 201

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Abstract. It can be divided into subjective and objective analysis on the sensory analysis of fruits. Subjective analysis almost based on sight, smell, touch, hearing and sense of taste, and the information of fruits will be analyzed when it was transmitted to brain after sensory cell receiving external stimuli. Objective analysis refers to the intelligent detection combined with modern technology, and the paper is mainly about fruit quality analysis based on near-infrared analytical technique. Otherwise, the differences and combinations between two analysis were also introduced.

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“…Rungpichayapichet, Mahayothee, Nagle, Khuwijitjaru, and Müller () used a portable VIS‐NIR photo‐diode array spectrometer (HandySpec Field 1000, tec5AG) to study mango fruit quality and maturity and good results were reported for SSC prediction, standard error of prediction (SEP) of 1.2% and a correlation coefficient ( R 2 ) of 0.90, for firmness prediction (SEP of 4.22 N and R 2 of 0.82), and for TA prediction (SEP of 0.38% and R 2 of 0.74). Marques, Freitas, Pimentel, and Pasquini () studying ‘Tommy Atkins’ mangoes used a portable MicroNIR 1700 to predict SSC, DM, TA, and firmness with low root mean square error of prediction (RMSEP), 0.92°Brix, 0.51%, 0.17%, and 12.2 N, respectively. Santos Neto, Assis, Casagrande, Cunha Júnior, and Teixeira () reported the use of a portable F‐750 to predict DM in ‘Palmer’ mango with good results, root mean square error of crossvalidation (RMSE CV ) of 8.3 g.kg −1 and RMSE cv of 8.8 g.kg −1 with a

R_{normalc}^{2}

of 0.86 and

R_{cv}^{2}

of 0.84.…”

Section: Introductionmentioning

confidence: 99%

Cold storage of ‘Palmer’ mangoes sorted based on dry matter content using portable near infrared (VIS-NIR) spectrometer

Neto

Leite

Oliveira

et al. 2018

J Food Process Preserv

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The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, RnormalP2 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1). Practical applications Although results can be found regarding the use of portable NIR spectrometers to estimate maturity in mango fruit, there are no studies stating the use of this method to sort fruit prior cold storage. Our results highlight that portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on dry matter (DM) content and this classification affects fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1) at the end of the storage period.

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Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer

Cited by 142 publications

References 28 publications

Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer

Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer

Nondestructive Determination of Fruit Based on Sensory and Near-Infrared Technology

Cold storage of ‘Palmer’ mangoes sorted based on dry matter content using portable near infrared (VIS-NIR) spectrometer

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