scholarly journals The performance of the C12880MA MEMS sensor for classification of the roasting level of coffee bean

2021 ◽  
Vol 924 (1) ◽  
pp. 012021
Author(s):  
A R I Ulinnuha ◽  
Z A Bahtiar ◽  
A R Nauri ◽  
I Rhamadan ◽  
R C Wulansari ◽  
...  
Keyword(s):  
Performance Test ◽  
Low Cost ◽  
Coffee Bean ◽  
Full Spectrum ◽  
Linear Discriminant ◽  
Mems Sensor ◽  
Coffee Beans ◽  
Mems Technology ◽  
Portable Sensor

Abstract The cupping test has been widely used to assess the roasting level of coffee to produce high-quality coffee beans. However, the method requires a longer process and sophisticated sensory analysis. The procedure could only be assessed by the certified panellist. Lately, commercial microelectromechanical system (MEMS) technology has been developed, which could be used for building a small spectrometer sensor. This gives the opportunity to adopt bench-top spectrometer sensing into the low-cost portable sensor. This research aims to study the performance test on the C12880MA MEMS sensor to determine the level of roasted coffee. A total of 90 samples from each 30 medium roasting level (Light to Medium, Medium, and Medium to Dark) was prepared. Spectrum data of samples were measured using a C12880MA sensor ranging from 312.162nm to 868.503nm. Linear Discriminant Analysis (LDA) was performed to classify the roasting level. The result showed that both LDA using full-spectrum and interval spectrum gave 100% accuracy with no falsely classified.

scholarly journals Environmental origin classification of coffee beans using infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012014
Author(s):  
Yusmanizar ◽  
A A Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Diffuse Reflectance ◽  
Confusion Matrix ◽  
Principal Component ◽  
Coffee Bean ◽  
Linear Discriminant ◽  
Coffee Beans ◽  
Explained Variance ◽  
Green Coffee Bean

Abstract Coffee is one of tropical agricultural products cultivated in many counties and consumed by people worldwide. The main purpose of this study is to employ the infrared spectroscopy technique to rapidly classify the environmental origins of green coffee bean samples. To achieve this purpose, diffuse reflectance spectral data of coffee samples were collected and acquired in wavelength rang of 1000 – 2500 nm. Classification models were established using principal component analysis (PCA) combined with linear discriminant analysis (LDA). The result showed that coffee bean sample can be classified based on their environmental origins with maximum total explained variance of the first two principal components is 97% (PC1 87% and PC2 10% respectively). Judging from the confusion matrix of the LDA, the classification accuracy reach 92%. It may conclude that infrared spectroscopy approach can be used to rapidly classify and sort coffee beans based on their geographical and environmental origins.

scholarly journals Uji Performansi Instrument Berbasis Teknologi Laser Photo-Accoustics Untuk Deferensiasi Kopi

2019 ◽  
Vol 4 (1) ◽  
pp. 618-627
Author(s):  
Giar Pramanda Putra ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Performance Test ◽  
Baseline Correction ◽  
Coffee Bean ◽  
Arabica Coffee ◽  
Robusta Coffee ◽  
Test Instrument ◽  
Partial Component ◽  
Coffee Beans ◽  
Pca Method

Abstrak. Penelitian ini bertujuan untuk memanfaatkan instrument berbasis teknologi Laser Photo Acoustics untuk membedakan biji kopi berdasarkan perbedaan daerah dan jenis kopi. Penelitian ini menggunakan softwere The Unscrambler X 10.5 Parameter penelitian meliputi penggunaan metode PCA (partial component analysis( dengan mengguanakan pretreatment baseline correction dan klasifikasi biji kopi beserta perbedaan daerah penghasil kopi dengan total 40 sampel tembakan laser antara lain 20 sampel biji kopi arabika dan 20 sampel biji kopi robusta. Hasil penelitian menunjukkan bahwa uji performansi alat laser photo acoustics ini mampu mendeteksi perbedaan kopi berdasarkan klasifikasi daerah penghasil kopi antara biji kopi daerah bener meriah dan  biji kopi daerah takengon, kemudian dengan melihar perbandingan perbedaan dengan klasifikasi biji kopi berdasarkan jenis kopi arabika dan robusta belum bias terdeteksi dengan alat ini dikarenakan beberapa factor salah satunya ialah sensor pembaca gelombang dari alat ini masih kurang peka terhadap deferensiasi jenis kopi robusta dan arabika.Performance Test Instrument Based on Laser Photo Acoustic Technology for Coffee DifferentiationAbstract. This study aims to utilize an instrument based on Laser Photo Acoustics technology to distinguish coffee beans based on different regions and types of coffee. This study uses the software The Unscrambler X 10.5 Research parameters include the use of the PCA method using pretreatment baseline correction and coffee bean classification along with differences in coffee producing regions with a total of 40 laser shot samples including 20 samples of arabica coffee beans and 20 samples of robusta coffee beans. The results showed that the performance test of the photo acoustics laser tool was able to detect differences in coffee based on the classification of coffee-producing regions between coffee beans and the coffee beans in the takengon area, then by comparing the differences with the classification of coffee beans based on Arabica and robusta coffee types could not be detected with this tool due to several factors, one of which is the wave reader sensor from this device is still less sensitive to the differentiation of robusta and arabica coffee types.

scholarly journals Design and Performance Test of the Coffee Bean Classifier

2021 ◽  
Author(s):  
Ansar Ansar ◽  
Sukmawaty Sukmawaty ◽  
Murad Murad ◽  
Surya Abdul Muttalib ◽  
Riyan Hadi Putra ◽  
...  
Keyword(s):  
Performance Test ◽  
Work Capacity ◽  
Operating Conditions ◽  
Coffee Bean ◽  
Working Capacity ◽  
Rotating Speed ◽  
Coffee Beans ◽  
Long Time ◽  
Effectiveness And Efficiency ◽  
And Performance

Nowadays, some coffee production centers are still classification manually, so it requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and performance of the coffee bean classifier that can accelerate the process of classification beans. The classifier used consists of three main parts, namely the frame, driving force, and sieves. Research parameters include classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was found at a rotational speed of 91.07 rpm and a 16° sieves angle with a classifier working capacity of 38.27 kg/h, the distribution of the seeds retained in the first sieve was 56.77 %, the second sieves was 28.12%, and the third sieves was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieves angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classification, namely small, medium, and large.

scholarly journals Design and Performance Test of the Coffee Bean Classifier

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1462
Author(s):  
Ansar ◽  
Sukmawaty ◽  
Murad ◽  
Surya Abdul Muttalib ◽  
Riyan Hadi Putra ◽  
...  
Keyword(s):  
Performance Test ◽  
Work Capacity ◽  
Operating Conditions ◽  
Coffee Bean ◽  
Working Capacity ◽  
Rotating Speed ◽  
Coffee Beans ◽  
Long Time ◽  
Effectiveness And Efficiency ◽  
And Performance

Currently, some coffee production centers still perform classification manually, which requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and test the performance of a coffee bean classifier that can accelerate the process of classifying beans. The classifier used consisted of three main parts, namely the frame, the driving force, and sieves. The research parameters included classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was a rotational speed of 91.07 rpm and a 16° sieve angle with a classifier working capacity of 38.27 kg/h: the distribution of the seeds retained in the first sieve was 56.77%, the second sieve was 28.12%, and the third sieve was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieve angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classifications, namely small, medium, and large.

scholarly journals Changes of physical properties of coffee beans during roasting

2012 ◽  
pp. 21-31 ◽  
Author(s):  
Marija Jokanovic ◽  
Natalija Dzinic ◽  
Biljana Cvetkovic ◽  
Slavica Grujic ◽  
Bozana Odzakovic
Keyword(s):  
Heating Time ◽  
Coffee Bean ◽  
Roasted Coffee ◽  
Colour Parameter ◽  
Coffee Beans ◽  
Roasting Process ◽  
Roasting Temperature ◽  
Physical Changes ◽  
Light Medium

The effects of heating time on physical changes (weight, volume, texture and colour) of coffee beans (Outspan and Guaxupe coffee) were investigated. The roasting temperature of both samples was 170?C and samples for analysis were taken at the intervals of 7 minutes during 40 minutes of roasting. Total weight loss at the end of the roasting process was 14.43 % (light roasted) and 17.15 % (medium to dark roasted) for Outspan and Guaxupe coffee beans, respectively. Significant (P < 0.05) changes in the coffee bean breaking force values were noted between the 7th and 14th minutes, and statistically not significant (P > 0.05) between the 35th and 40th minutes of the roasting. According to the L* colour parameter as a criterion for the classification of roasted coffee colour (light, medium, dark), the Outspan sample was medium and Guaxupe sample was dark roasted.

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

scholarly journals Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

2021 ◽  
Vol 14 (5) ◽  
pp. 440
Author(s):  
Eirini Siozou ◽  
Vasilios Sakkas ◽  
Nikolaos Kourkoumelis
Keyword(s):  
Infrared Spectroscopy ◽  
Low Cost ◽  
Diclofenac Sodium ◽  
Reference Method ◽  
Sodium Content ◽  
Attenuated Total Reflection ◽  
Accuracy And Precision ◽  
Significant Difference ◽  
Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ianthe A. E. M. van Belzen ◽  
Alexander Schönhuth ◽  
Patrick Kemmeren ◽  
Jayne Y. Hehir-Kwa
Keyword(s):  
Intratumor Heterogeneity ◽  
Precision Oncology ◽  
Single Nucleotide Variants ◽  
Full Spectrum ◽  
Single Nucleotide ◽  
Sequencing Technologies ◽  
Cancer Genomes ◽  
Genomic Aberrations

AbstractCancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.

Sign in / Sign up

Export Citation Format

Share Document