A Fast and Inexpensive Chemometric-Assisted Method to Identify Adulteration in Acai (Euterpe oleracea) Using Digital Images

Araújo, Alisson; Marinho, Weverton; Gomes, A. A.

doi:10.1007/s12161-017-1127-4

Cited by 21 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Glass, quartz or prismatic PTFE cuvettes 17,[29][30][31] and flow cells 32,33 have been used similarly to spectrophotometry involving specific colorimetric reactions, replacing exclusively the detector. Other common holders have included glass, plastic and lab-made polytetrafluorethylene (PTFE) Petri plates 25,27,28,[34][35][36][37][38][39][40][41][42][43][44][45][46] have been the most employed holder type to dispose the samples directly without pretreatment. PTFE chambers of automated Flow-Batch Analyzers 26,47 have been also used to prepare samples and calibration solutions in flow into the mixing chamber.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

“…Araújo, Marinho and Gomes 40 identified açaí ( Euterpe oleracea ) samples adulterated with cassava and wheat flours. They used grayscale+RGB + HSI histogram and one‐class modeling techniques.…”

Section: Applicationsmentioning

confidence: 99%

“…The use of white light enables the differentiation between different tones, hues, and shades when histograms are constructed from a digital image. Common sources include fluorescent lamps (either external or internal to a scanner 15,21,25,28,32,[34][35][36][37][38][39][42][43][44]46,48,49,[54][55][56][57] ) and white-light LED (light-emitting diode) lamps 26,27,30,33,40,41,45,47,58,59 or LED strips, 17,31,50 and they must be suitably disposed in the system in order to avoid shadows and illumination gradients under the ROI. Ultraviolet (UV) lamps, 18,51 near infrared (NIR) LEDs, 29 internal flash lamp, 52,61 white-light-transilluminator, 53 and luminoscope 60 have been also used.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

See 2 more Smart Citations

Chemometrics‐assisted color histogram‐based analytical systems

Diniz

2020

Journal of Chemometrics

View full text Add to dashboard Cite

This review systematizes for the first time the here called "Chemometricsassisted color histogram-based analytical systems" under the acronym CACHAS. A comprehensive discussion of the fundamentals, general characteristics, and applications are presented in order to direct practical aspects for developing methods that use color histograms as input data for the construction of chemometric models. For this, analytical information is rapidly acquired using a simple image acquisition device (such as digital camera, webcam, scanner, or smartphone), without need for any image processing, other than extraction of the histograms, which are constructed by counting the frequency distribution of the different color tones (conveniently specified through a color space) of all pixels delimited by a region of interest in a digital image. As a consequence, CACHAS have been increasingly used for qualitative and quantitative purposes in food, agricultural, forensic, biomedical, and microbiological analysis, reinforcing the principles of green analytical chemistry.

show abstract

Section: Instrumentation For Cachasmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Instrumentation For Cachasmentioning

confidence: 99%

See 1 more Smart Citation

Chemometrics‐assisted color histogram‐based analytical systems

Diniz

2020

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…For instance, the application of the one-class partial least squares (OCPLS) approach in NIR spectral data analysis for classifying the geographical origin of Chinese herbs yielded excellent results [8]. Another study demonstrated the potential of the data-driven soft independent modeling of class analogy (DD-SIMCA) approach in the non-targeted detection of wheat flour and cassava adulterants in acai (Euterpe oleracea) [9].…”

Section: Introductionmentioning

confidence: 99%

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Faqeerzada

Lohumi

Joshi

et al. 2020

Foods

View full text Add to dashboard Cite

Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

show abstract

“…Many complex problems in chemistry such as geographic tracing, 1 discrimination between different brands, 2 food authenticity, adulteration, and quality control, 3 to name a few, can be solved using pattern recognition 4 . The methodologies developed for these purposes usually employ spectrometric and chemometric techniques.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization for variable selection in discriminant linear analysis

Pontes

Araújo

Marinho

et al. 2020

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

A new algorithm using ant colony optimization (ACO) for selection of variables in linear discriminant analysis (LDA) is presented. The role of ACO is explored in the context of LDA classification in which spectral variable multicollinearity is a known cause of generalization problems. The proposed ACO-LDA presents a metaheuristic that mimics the ant's cooperative behavior, randomly depositing pheromones at vector elements corresponding to the most relevant variables. Such cooperative ant-like behavior, which is absent in the genetic algorithm, increases the probability of discarding noninformative variables, favoring construction of more parsimonious models than genetic algorithm-linear discriminate analysis (GA-LDA). The classification performance of ACO-LDA is assessed in two case studies: (i) classification of edible vegetable oils (with respect to base oil) via ultraviolet-visible (UV-Vis) spectrometry and (ii) simultaneous classification of tea samples with respect to type and geographic origin via near-infrared (NIR) spectrometry. In the first study, ACO-LDA was tested in a data set involving wide absorption bands in the UV region with low-resolution and strong spectral overlapping. In the second study, its capacity to manage a data matrix with high dimensionality was evaluated. In both studies, ACO-LDA selected a small subset of variables, which led to correct classifications for almost all of the samples, achieving a performance level similar to the well-established partial least squares-discriminant analysis (PLS-DA), and considerably better than GA-LDA. The use of ACO to select LDA classification variables can minimize generalization problems commonly associated with multicollinearity.

show abstract

A Fast and Inexpensive Chemometric-Assisted Method to Identify Adulteration in Acai (Euterpe oleracea) Using Digital Images

Cited by 21 publications

References 27 publications

Chemometrics‐assisted color histogram‐based analytical systems

Chemometrics‐assisted color histogram‐based analytical systems

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Ant colony optimization for variable selection in discriminant linear analysis

Contact Info

Product

Resources

About