COMPARISON OF A DOUBLE COMPRESSION TEST FOR THE PREDICTION OF SENSORY TEXTURE ATTRIBUTES OF COOKED RICE TO A SINGLE COMPRESSION TEST

A number of instrumental means to predict cooked rice texture has been reviewed. However, Little information has been reported as to a direct comparison for the two typical compression tests (double vs. single) in performance to predict cooked rice texture. This study was aimed at exploring the performance of a double compression (DC) and single compression (SC) test for predicting cooked rice texture and a potential use of Partial Least Square Regression (PLSR) for developing predictive models of specific texture attributes. Four different cultivars of rice stored for 32 weeks were used in this study. A total of 11 texture attributes of cooked rice in five stages were profiled by 7 trained descriptive panelists. Five sensory attributes (manual stickiness, initial cohesion, adhesion to lips, toothpull and hardness) showing significant differences by descriptive panel between rice samples over different storage time were finally predicted. The models by a DC and SC test were robust as well as discriminative and equivalent in performance for predicting texture of cooked rice. Both tests allowed the satisfactory prediction for adhesion to lips and toothpull and the moderate prediction for manual stickiness, initial cohesion and hardness. However, considering that it is routine assessments for rice breeders to predict mechanically rice texture quality, a SC test would have the advantage being less time-consuming over a DC test.

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Sensory evaluation of commercial ready-to-eat rice between trained panelist and consumer

British Food Journal ◽

10.1108/bfj-11-2016-0530 ◽

2018 ◽

Vol 120 (2) ◽

pp. 367-377 ◽

Cited By ~ 4

Author(s):

Yong-Suk Kwon ◽

Se-young Ju

Keyword(s):

Brown Rice ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Consumer Acceptability ◽

Content Type ◽

White Rice ◽

Cooked Rice ◽

Rice Samples

Purpose The purpose of this paper is to examine descriptive sensory characteristics and consumer acceptability of eight commercial ready-to-eat cooked rice samples by 8 trained panelists and 50 consumers. Design/methodology/approach A total of 24 descriptive attributes for appearance, odor/aroma, taste/flavor, and texture were developed. Also Consumer Acceptability (CA) was performed for overall liking, appearance, flavor, and texture liking. All statistical analyses were using analysis of variance, principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square regression (PLSR). Findings The overall liking score for the cooked white rice from C brand was the highest (6.43) among the eight samples. Three groups of eight commercial ready-to-eat cooked rice samples were obtained from PCA and HCA. The samples of cooked white rice from C, N, and O brand characterized by intactness, starch odor, translucency, whiteness, and glossiness were located on to the positive PLS 1, whereas the samples of cooked white rice from D and E brand characterized by scorched odor, cohesiveness, stickiness, and moistness were located on the negative side of PLS 2 in the PLSR analysis. Originality/value Further studies on the improvement of sensory quality for brown rice are necessary to increase CA in terms of health functionality of brown rice.

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Different or similar? Acceptability of aseptic-packaged cooked rice among consumers, researchers and experts

British Food Journal ◽

10.1108/bfj-09-2014-0330 ◽

2015 ◽

Vol 117 (5) ◽

pp. 1564-1580

Author(s):

Han Sub Kwak ◽

Misook Kim ◽

Yoonhwa Jeong

Keyword(s):

Descriptive Analysis ◽

Strong Relationship ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Product Development Process ◽

Rice Cake ◽

Content Type ◽

Cooked Rice ◽

First Time

Purpose – The purpose of this paper is to compare the acceptance ratings and drivers of liking and disliking attributes of aseptic-packaged cooked rice by consumers, researchers and experts. Design/methodology/approach – Descriptive analysis (DA) was conducted using trained panelists. Acceptability was measured by consumers, researchers and experts. The results of DA and acceptability were analyzed using partial least square regression. Findings – There was no strong relationship among the three groups in their rating patterns for the samples (r=−0.342-0.445). The liking factors for each group were as follows: consumers (rice cake flavor and moisture), researchers (wet wood flavor and whiteness) and experts (wet wood flavor and size of rice). The disliking factors for each group were as follows: consumers (wet wood flavor and brown particle), researchers (moisture) and experts (old rice aroma). The consumers, researchers and experts seemed to have different acceptances and key descriptive attributes for aseptic-packaged cooked rice. Research limitations/implications – The consensus by researchers during the product development process required caution with regard to the fact that the evaluation by the researchers could be different from what consumers or experts prefer. Practical implications – Setting-up in-house panelists group would be minimized the discrepancy between consumers and researches. Originality/value – This study contributes to understanding of the acceptability by food researchers and comparing to consumers and experts for the first time in sensory field.

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Effect of water activity and temperature on growth of Penicillium citreoviride and Penicillium citrinum on MiGao (rice cake)

Canadian Journal of Microbiology ◽

10.1139/w06-096 ◽

2007 ◽

Vol 53 (2) ◽

pp. 231-236 ◽

Cited By ~ 4

Author(s):

Ying Ji ◽

Kexue Zhu ◽

Haifeng Qian ◽

Huiming Zhou

Keyword(s):

Water Activity ◽

Penicillium Citrinum ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Growth Responses ◽

Rice Cake ◽

Cooked Rice ◽

Different Temperatures ◽

Optimum Water

Contamination by mold is a serious problem in steam-cooked rice cake, a traditional Chinese food. Growth responses to different temperatures and water activity values for Penicillium citreoviride and Penicillium citrinum , two of the most common molds, were investigated. Partial least square regression analysis showed that the growth of the two fungi did not differ in response to changes in water activity and temperature. Optimum water activity for growth was 0.90 and optimum temperatures for growth were 30 °C in most cases.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽

10.3390/molecules26082342 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2342

Author(s):

Nikolaos Nenadis ◽

Maria Papapostolou ◽

Maria Z. Tsimidou

Keyword(s):

Essential Oil ◽

Radical Scavenging Activity ◽

Pearson Correlation ◽

Radical Scavenging ◽

Methyl Eugenol ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Positive Contribution ◽

Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

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