scholarly journals Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Thierry Delatour ◽  
Florian Becker ◽  
Julius Krause ◽  
Roman Romero ◽  
Robin Gruna ◽  
...  
Keyword(s):  
Milk Powder ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Handheld Devices ◽  
Portable Devices ◽  
Food Fraud ◽  
Start Up ◽  
Orthogonal Signal Correction

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

scholarly journals Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

2021 ◽  
Vol 18 (20) ◽  
pp. 31
Author(s):  
Zulfahrizal Zulfahrizal ◽  
Agus Arip Munawar
Keyword(s):  
Prediction Models ◽  
Quality Attributes ◽  
Quality Parameters ◽  
Fat Content ◽  
Prediction Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Cocoa Beans ◽  
Orthogonal Signal Correction

This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

scholarly journals PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 286
Author(s):  
Ofélia Anjos ◽  
Ilda Caldeira ◽  
Tiago A. Fernandes ◽  
Soraia Inês Pedro ◽  
Cláudia Vitória ◽  
...  
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Volatile Phenols ◽  
Near Ir ◽  
Liquid Liquid Extraction ◽  
Calibration Models ◽  
First Time

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

scholarly journals Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

2020 ◽  
Vol 8 ◽  
Author(s):  
Roberta Risoluti ◽  
Giuseppina Gullifa ◽  
Stefano Materazi
Keyword(s):  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
The Novel ◽  
Screening Platform ◽  
Quality Of Milk ◽  
Different Origins ◽  
Non Destructive

In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.

scholarly journals Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

2016 ◽  
Vol 23 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Wen-Zhi Zeng ◽  
Jie-Sheng Huang ◽  
Chi Xu ◽  
Tao Ma ◽  
Jing-Wei Wu
Keyword(s):  
Salt Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Irrigation District ◽  
Hyperspectral Reflectance ◽  
Soil Salt Content ◽  
Soil Salt ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Abstract For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

Cultural intelligence of entrepreneurs and international network ties

2015 ◽  
Vol 38 (4) ◽  
pp. 421-436 ◽  
Author(s):  
Peerayuth Charoensukmongkol
Keyword(s):  
Cultural Intelligence ◽  
Export Performance ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Foreign Firms ◽  
Content Type ◽  
Foreign Competitors

Purpose – This paper aimed to investigate whether the cultural intelligence (CQ) of entrepreneurs is associated with the quality of the relationships firms develop with foreign networks. Design/methodology/approach – The samples include small and medium manufacturing firms in Thailand. Data were collected with a self-administered questionnaire survey. A list of 1,000 firms was randomly selected from the directory of Thai exporters. A total of 129 surveys were returned. Partial least square regression was used to analyze the data. Findings – The results revealed a positive association between the CQ of entrepreneurs and the quality of the relationships that small and medium enterprises (SMEs) had with foreign customers, foreign suppliers and foreign competitors. The quality of the relationships was also associated positively with export performance. However, there was no significant evidence for the role of the quality of relationships with foreign competitors in export performance. Research limitations/implications – The use of cross-sectional data makes it difficult to claim causality between the constructs. Moreover, the CQ and export performance measures that use subjective evaluation may cause bias. The small sample size also limits the generalizability of the results. Practical implications – The results suggested that CQ is a key capability entrepreneurs must develop to conduct business more successfully in foreign markets. Social implications – Because SMEs are considered a key driver of a country’s economic development, CQ training could be an important choice on which the government should focus. Furthermore, as the world economy is more integrated, CQ training can significantly help people improve cross-cultural communication skills which are essential for them to be successful in today’s globalized economy. Originality/value – Despite the increasing popularity of CQ research, evidence for its contribution to the ability of entrepreneurs to develop good relationships with foreign firms is lacking. The main contribution of this study is to bridge this research gap by providing empirical evidence.

Analysis of Total Polar Material in Selected Indian Snack’s Fried Oil

2021 ◽  
Vol 13 (2) ◽  
pp. 561-570
Author(s):  
R. Kumar ◽  
B. Bhattacharya ◽  
T. Agarwal ◽  
S. Chakkaravarthi
Keyword(s):  
Partial Least Square ◽  
Frying Oil ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Good Prediction ◽  
Polar Material ◽  
Total Polar Material ◽  
Prediction Capability ◽  
Fried Oil

The study was envisaged to examine the quality of frying oil used by street food vendors for two of the most popular food items viz. Samosa and Jalebi in India. Changes in the quality of frying oil were analysed by analysing the total polar material (TPM) content in the oil using an oil tester and Attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Total 143 oil samples were collected at different frying times, i.e. 0, 2 and 4 h from five different Samosa and Jalebi vendors. In both the fried food oil samples, TPM content increased with increasing frying time. The TPM content in the 4 h fried oil samples of Jalebi was significantly (p< 0.001) higher than the samosa fried oil. Partial Least Square Regression (PLS) model based on the 1st derivative FTIR spectra exhibited good prediction capability for TPM values with a high regression coefficient (R2 ≥ 0.99) and low root mean square error (RMSE).

Internal Quality Estimation of Watermelon by Multiple Acoustic Signal Sensing

2006 ◽  
Vol 321-323 ◽  
pp. 1209-1212 ◽  
Author(s):  
Kang Jin Lee ◽  
Wank Yu Choi ◽  
Gi Young Kim ◽  
Suk Won Kang ◽  
Sang Ha Noh
Keyword(s):  
Evaluation System ◽  
Noise Removal ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Internal Quality ◽  
Internal Defect ◽  
Sorting System ◽  
Non Destructive

Watermelons are usually sorted by theirs weight and internal quality. Some automated watermelon weight sorters have been developed and operated in watermelon production areas. However, inspection of internal quality of watermelon is still performed by manually. Principal method of identifying internal defect of watermelon is analyzing the percussion sound of watermelon by human experts. Development of non-destructive evaluation technique for internal quality of watermelon is required to reduce human decision errors. The objective of this study was to develop a non-destructive sorting system which can detect internal defect of watermelons. The internal defect evaluation system has a constant-force hitting hammer to generate the acoustic sound, a multi-point sound signal acquiring system, a noise removal circuit, and a signal processing and quality evaluation program. An internal quality prediction model by PLSR (Partial Least Square Regression) was developed by analyzing the percussion sound of watermelons. Using the developed model, the prediction result shows that the overall prediction accuracy was 90.1%, and severely defected watermelons were identified perfectly.

scholarly journals Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt

2021 ◽  
Vol 13 (18) ◽  
pp. 10408
Author(s):  
Salah Elsayed ◽  
Mohamed Gad ◽  
Mohamed Farouk ◽  
Ali H. Saleh ◽  
Hend Hussein ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Indicators ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Water Quality Indicators ◽  
Chl A ◽  
Strong Relationships

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R2 = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R2 = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R2 = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R2 = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI620,610,622) showed the highest R2 of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R2 = 0.85) and validation (reach up R2 = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

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