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Purpose
– The purpose of this paper is to develop FT‐NIR technique for determination of moisture content in bael pulp.
Design/methodology/approach
– Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 70 to 95 per cent (wb). The prediction models based on partial l...
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Citations
... Spectral pre-processing was done using OPUS software 7.8, according to the method adapted from Bag, Srivastav and Mishra. 27 This was done to reduce the prediction errors associated with spectral noise and the influences of temperature changes, particle size differences, light diffusion and baseline shifts on the NIR spectra while increasing signal from chemical information. 28,29 Before calibration, the raw spectra were treated with a combination of multiple pre-processing techniques, including first derivative, second derivative, and standard normal variate (SNV). ...
Food fortification is one strategy for addressing micronutrient deficiencies among the population groups at risk. Non-compliance with fortification standards hinders the success of fortification programs. This is due to a lack of techniques to rapidly check the amounts of the added fortificants. Fourier transform - near-infrared (FT-NIR) spectroscopy is a fast and reliable technique that would be used to ensure adherence to requirements. This study aimed to investigate the potential of using FT-NIR spectroscopy to predict the amount of retinol in fortified maize flour. 150 fortified maize flour samples were used in this study. Partial least squares regression (PLS-R) was used to build calibration models based on the retinol reference values obtained by high-performance liquid chromatography (HPLC), and fortified maize flour NIR spectra acquired from the FT-NIR spectrophotometer. Two calibration models were developed to predict retinol above and below 1.0 mg/kg. The performance metrics of model one developed to predict retinol < 1.0 mg/kg were: R2c = 0.81, RMSEE = 0.08, RPD = 2.29 and R2v = 0.82, RMSEP = 0.09, RPD = 2.07 for the calibration and validation, respectively. The second model developed to predict retinol ≥ 1.0 mg/kg had the following performance metrics: R2c = 0.93, RMSEE = 0.16, RPD = 3.58 and R2v = 0.81, RMSEP = 0.22, RPD = 2.43 for the calibration and validation, respectively. Overall, the findings demonstrated that FT-NIR spectroscopy can be utilised to reliably predict retinol levels in fortified maize flour samples. FT-NIR spectroscopy, by replacing time-consuming and laborious wet chemistry laboratory procedures, has the potential to be used for rapid regulatory monitoring of fortification compliance for a large number of samples.
... Some studies showed the feasibility of VIS/NIR for the internal quality detection of large, thick-peeled fruit [20], but only for TSS, not for other quality indexes like water content or granulation. VIS/NIR is an efficient way to detect the water content of small fruit [34,35], but of unknown usefulness for detecting the water content of large, thick-peeled fruit like pomelo. On the other hand, as with pomelo, granulation is a major problem for oranges, and this has been extensively investigated [36]. ...
Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.
... A similar approach to estimate the moisture content of Golden Delicious apples using NIR spectroscopy revealed that decrease in moisture content during time greatly influenced the density of apples in the linear regression model (Vesali et al. 2011). Bag et al. (2011) confirmed the potential of Fourier transform-NIR spectroscopy for rapid estimation of moisture content in bael pulp based on PLSR model using wavelength region between 800 and 2500 nm under diffused reflectance mode. The NIR spectra pre-processed with min-max normalization (MMN) method was found to be more suitable based on the acquired maximum coefficient of determination value (R 2 = 0.993). ...
Daily consumption of fruits has rendered sophisticated techniques for accurate evaluation of its quality and how it can be initiated in a more rapid way has become a state-of-art for the fruit processors and food safety agencies. With the advent of non-destructive techniques in the past two decades, the quality and safety assessment procedure has become more reliable and non-invasive with reasonable accuracy. They have significantly contributed to the quality and safety inspections of fruits allowing for minimal expense of processing systems. The current review provides an overview of the technical aspects used for evaluating the essential physicochemical components in fruits. The non-destructive techniques described in this study primarily constitute near-infrared (NIR) spectroscopy and hyperspectral imaging in addition to their respective data analysis, classification, and calibration methods. First, the individual mechanism is outlined considering spectral/image acquisition mode followed by the extraction of information from the acquired data. Then, potentials of different chemometrics, image processing, and hyperspectral data reduction and feature extraction methods used in both the techniques have been epitomized for predicting the fruit maturity and other internal components such as firmness, acidity, phenolic content, soluble solids, vitamins C, moisture content, defects, fecal contamination, starch index, sugar content, and dry matter. The literature in this overview portrays the potentials of different chemometric and multivariate image analysis methods used in near-infrared spectroscopy and hyperspectral imaging, respectively, as excellent quality assessment aspects for fruits. However, further improvements are required in handling the voluminous data in industrial applications. The application of both the techniques is limited to a few fruits and further research is required for exploiting their implications.
... FT-NIR spectroscopy could be used for rapid detection of moisture content without destruction of samples. The technique had successfully determined the MC of meat [20], green tea granules [21], tea powder [22], freeze-dried materials [23], bael-pulp [24], epoxy resins and fiber-reinforced composites [25], and rice pasta [26]. However, study on MC in rice kernel using NDE-based FT-NIR spectroscopy is limited, for rice cultivar originated from west Sumatra, Indonesia. ...
Determination of indices of the indigenous west Sumatran rice varieties was done to rapidly evaluate its moisture contents (MC) by means of non-destructive evaluation. The objective of this study was to identify the MC of two indigenous rice from west Sumatra, Indonesia, namely Junjuangan and Mundam, cultivars. The evaluation was rapidly performed by means of non-destructive evaluation using 1000-2500 nm short wave infrared (SWIR) spectral assessment. The paddy grains samples with identical MC were put into 10 cm petri dish and measured using SWIR spectrophotometer. The grains’ actual MC was then measured by primary method, based on weight measurement. In this study, the spectral data of the grains was then processed by means of Principal Component Analysis (PCA) before correlated with its MCs by Partial Least Square (PLS) method. The model calibration obtained for SWIR spectrophotometer showed correlation of 0.826 and 0.955, with root mean squared error calibration (RMSEC) of 2.97 and 1.4 for Junjuangan and Mundam rice respectively. Moreover, model validation produced correlation of 0.788 and 0.968, RMSEP of 3.8 and 1.29, and bias of 0.193 and 0.171 for Junjuangan and Mundam rice, respectively. The results indicated that the MC of paddy grains could be precisely identified by means of non-destructive evaluation using spectral analysis.
... Several works have been developed applying NIR and MIR spectroscopies to evaluate quality and to quantify different chemical compounds in food products, food authenticity, and adulterations. Infrared analysis has been successfully used to quantify total phenolic compounds and condensed tannins contained in grape seed, to evaluate ripeness of white grape, and to a qualitative and quantitative evaluation of grape berries at various stages of development (de Oliveira et al. 2014;Bag, Srivastav and Mishra 2011;Shiroma and Rodriguez-Saona 2009;Hell et al. 2016;Martelo-Vidal and Vázquez 2014;Mendes et al. 2015;Li et al. 2013;Ignat et al. 2012;Wang and Xie 2014;Kyraleou et al. 2015;Giovenzana et al. 2015;Musingarabwi et al. 2016). ...
Grape juice is rich in bioactive compounds that are known for beneficial effects, such as prevention of cancer and diabetes. The determination of the content of these compounds has been carried out following several protocols which generated toxic waste. In this context, infrared spectroscopy in near (NIR) and mid- (MIR) regions were applied for the determination of total phenolic and anthocyanin content (TPC and TAC, respectively) in grape juice for being a rapid green methodology. Calibration models were built using partial least squares regression (PLSR) to predict TPC and TAC in grape juice. MIR and NIR had a similar satisfactory performance to predict TAC presenting low RMSEP (4.22 mg/100 mL and 4.44 mg/100 mL). In TPC prediction, MIR presented a RMSEP (0.21 mg GAE/mL) slightly better in comparison to the one presented by NIR (0.37 mg GAE/mL). Errors can be considered acceptable for the determination of TPC and TAC in grape juice, qualifying both infrared techniques to replace traditional wet methodologies, with the advantage of being environmentally friendly.
... Chemometrics (multivariate statistical techniques) assists the spectrum analysis, involving regression techniques (PLS, partial least squares; PCR, principal components regression; LS-SVM, least square support vector (Nicolaï et al., 2007;Pissard et al., 2013). Several studies have been performed applying NIR spectroscopy to evaluate quality attributes of fruits, as pear (soluble solids and firmness), passion fruit (soluble solids, titratable acidity, ascorbic acid content, ethanol concentration, peel firmness and pulp percentage), apple (firmness and soluble solids), plum (soluble solids and firmness), (Liu, Fu, & Cheng, 2007;Maniwara et al., 2014;Mendoza, Lu, Ariana, Cen, & Bailey, 2011;Paz, Sanchez, Perez-Marin, Guerrero, & Garrido-Varo, 2008), or to quantify some parameters in guava (soluble solids) and bael (moisture) pulps (Bag, Srivastav, & Mishra, 2011;Devia et al., 2015). ...
... Moisture content for both pulps presented RMSEP = 0.4% or less and R 2 val next to 0.93. Bag et al. (2011) and Torres, Perez-Marin, De la Haba, and Sanchez (2015) found a RMSEP around 0.5% for bael pulp (R 2 val = 0.97) and raf tomato moisture, respectively, that qualifies the models ( Fig. 2A and 2C) developed in this study to be used to quantify moisture in guava and passion fruit pulps. ...
The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp.
... 19 However, it is rarely used to determine pulp properties. FT-NIR spectroscopy has been used to determine the moisture content of bael pulp 20 and to estimate the percentage (%) of dry matter of fruit and vegetables, for example, avocado 18 , pear 21 and sugar beet pulp. 22 Wedding et al. 18 developed a calibration model for whole intact "Hass" avocado fruit encompassing fruit from three consecutive years and reported the following predictive statistics: coefficient of determination (r 2 ) of 0.89 and root mean square error of prediction (RMSEP) of 1.43% dry matter. ...
Fourier transform near infrared spectroscopy was used as a non-invasive technique for the determination of dry matter and soluble solids of durian pulp. A set of 25 fruit was randomly harvested every 10 days, starting from 80 days until 127 days after the onset of fruit development covering six levels of maturity (80 days, 90 days, 100 days, 110 days, 120 days and 127 days). After applying ethephon on the fruit stems, the fruits were kept for 3 days at room temperature and allowed to ripen. Only the pulp of the durian was scanned. The dry matter and soluble solids reference values of the samples were determined by a hot-air-oven method and using a refractometer, respectively. Prediction models using half the samples related the spectral data, and dry matter and soluble solids data were subsequently established using partial least-squares regression and validated using the other half of the samples in a prediction set. A full cross-validation was also generated using all 149 samples. Both the half-of-the-samples model and the all-sample model were then validated using a true validation set of samples collected in a later year. When tested against the validation half of the samples, the halfof- the-samples model predicted dry-matter content with a coefficient of determination (r2) and root mean square error of prediction (RMSEP) of 0.89 and 3.60%, respectively, and for soluble solids content 0.55 and 1.63 °Brix (Bx), respectively. When tested on samples from a later season, the model for dry-matter content returned an r2, RMSEP and bias of 0.26, 6.10% and 2.16%, respectively, and for soluble solids content 0.27, 1.25 °Bx and 1.09 °Bx, respectively. The cross-validated model for dry matter yielded a slightly better r2 and root mean square error of cross-validation (RMSECV) of 0.90 and 3.58%, respectively, however, the model for soluble solids did not provide a better r2 and RMSECV: 0.51 and 1.81 °Bx, respectively. When tested on samples from a later season, the cross-validated models gave, r2, RMSEP and bias of 0.15, 5.17% and 1.49%, respectively, for dry-matter content, and for soluble solids content 0.37, 1.32 °Bx and 1.23 °Bx, respectively. The poor results obtained when predicting dry matter in samples in later seasons indicate that samples from several seasons must be included in the set of calibration samples. This is the first report on the application of NIR spectroscopy to evaluate the dry matter and soluble solids of durian pulp and could be useful to customers, exporters, importers and also postharvest technologists. However, prediction accuracy was not demonstrated in the model for durian pulp soluble solids, possibly because of the effect of ethephon applied after harvesting to induce ripening within 3 days to make the fruit suitable for consumption. In addition, it was found that the vibration bands of cellulose and fat, and those of aromatic, CH2 and sucrose highly affected the predictions of dry matter and soluble solids in the durian pulp, respectively.
The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.
Spectrum preprocessing is an essential component in the near‐infrared (NIR) calibration. However, it has mostly been configured arbitrarily in the literature and calibration applications. In this paper, a systematic evaluation framework was proposed to quantify the effect of preprocessing, where repeated cross‐validation and evaluation are involved. As many as 108 preprocessing schemes were gathered from the literature and were tested on 26 different NIR calibration problems. Using the evaluation framework, appropriate schemes can be found for several datasets, reducing the root mean square error of prediction (RMSEP) by 50%–60% compared with using the raw spectrum. However, the influence of preprocessing is highly data‐dependent, and no universal solution could be found. Taking the effectiveness and correlation into consideration, Savitzky‐Golay (SG), SG1D, and SG1D + vector normalization (VN)(/standard normal variate [SNV]) are worth testing first. Nevertheless, the heterogeneity at both the dataset level and sample level demonstrated the necessity of a complete evaluation. Our scripts are available at https://github.com/jiaoyiping630/spectrum-preprocessing. A systematic evaluation of preprocessing methods in NIR calibration on 26 calibration problems. As many as 108 preprocessing schemes were compared and analyzed. An open‐source implementation for evaluating preprocessing schemes on new datasets.
