Penentuan Kandungan Padatan Terlarut Buah Jeruk BW secara Tidak Merusak Menggunakan Near Infrared Spectroscopy

Buah jeruk BW sangat terkenal di Provinsi Lampung. Untuk mengoptimalkan sistem pengkelasan buah jeruk BW diperlukan sebuah metode yang dapat mengukur kandungan padatan terlarut (KPT) buah jeruk BW secara tidak merusak produk. Pada penelitian ini, dilakukan pemeriksaan terhadap potensi metode near infrared (NIR) spectroscopy pada pengukuran KPT buah jeruk BW secara tidak merusak. Sebanyak 86 sampel, masing-masing 43 buah sampel untuk membangun model kalibrasi dan uji validasi digunakan pada penelitian ini. Spektra buah jeruk diambil dengan menggunakan spectrometer portable yang beroperasi pada panjang gelombang 300-1100 nm. Spektra diambil pada dua posisi yang berbeda pada teknik diffuse reflectance. Unscrambler digunakan untuk menginvestigasi hubungan antara spektra dan KPT buah jeruk BW dengan membangun model kalibrasi. Hasil penelitian ini menunjukkan model kalibrasi terbaik diperoleh pada smoothing spektra pada panjang gelombang 700-990 nm dengan R2=0.92 dan SEC=0.36. Validasi model menunjukkan model kalibrasi memiliki nilai bias dan SEP yang kecil. Dengan menggunakan tingkat kepercayaan 95%, uji t-test menunjukkan tidak terdapat perbedaan yang nyata antara KPT yang diukur menggunakan refraktometer dan KPT yang diprediksi oleh NIR spectroscopy

Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

UV-Vis spectroscopy has been used as a promising method for coffee quality evaluation including in authentication of several high-economic coffee types. In this paper, we have compared the abilities of linear discriminant analysis (LDA) and support vector machines classification (SVMC) methods for Luwak coffee classification. UV-Vis spectral data of 50 samples of pure Luwak coffee and 50 samples of pure non-Luwak coffee were acquired using a UV-Vis spectrometer in transmittance mode. The results show that UV-Vis spectroscopy combined with LDA and SVMC was an effective method to classify Luwak and non-Luwak coffee samples. The classification result was acceptable and yielded 100% classification accuracy for both LDA and SVMC methods. However, due to the simplicity and volume of the required calculation, in this present study LDA method is superior to SVMC method

Studi Penggunaan Kmno4 Untuk Memperpanjang Umur Simpan Pisang Muli

The purpose of this research is to investigate the influence of KMn Asoxidizingethylene and to evaluate theeffective of KMn to extend the shelf life of bananas. This research was conductedusing a single treatment with fourlevels of giving mass that is 1 g, 5g, 10g, and a control without KMn, with ranges of banana's weight was 400g.The result of the research showing that KMnasan oxidizingethylenebythe carrierfroma mixture of clayandricehusk ashin the storage ofbananas has positive influencein the process ofstorage. The most effective treatment is5 gram satseven days of shelf life and KMn which is placed beside the material is not effectively used because itcan not completely absorbethylene

Studi Penggunaan Uv-vis Spectroscopy Dan Kemometrika Untuk Mengidentifikasi Pemalsuan Kopi Arabika Dan Robusta Secara Cepat

There are two popular coffees in Indonesia, namely Arabica and Robusta coffees. Arabica coffee has a better quality than Robusta do. This research aimed to identify the purity of Arabica coffee; and Robusta as mixture ingredient, by using technology of UV-Vis spectroscopy and multivariate analysis, with a method of soft independent modelling of class analogy (SIMCA) and principal component analysis (PCA). The research was conducted using coffee powder with size 0,297 millimeters (50 mesh).The research used 100 samples; sample 1-50 (1 g of Arabica), sample 51-60 (0,8 g of Arabica and 0,2 of Robusta), sample 61-70 (0,7 g of Arabica and 0,3 g of Robusta), sample 71-80 (0,6 g of Arabica and 0,4 of Robusta) sample 81-90 (0,5 g of Arabica and 0,5 g of Robusta), sample 91-100 (0,4 g of Arabica and 0,6 g of Robusta). The result of classification showed that method of PCA and SIMCA are able to classify the mixture of pure Arabica. PC1 explained 77% various datas, and PC2 explained 10% various datas, whilst from data classification SIMCA obtained the precentage score onaccuracy 56%, sensitivitas 58%, and spesifisitas 0%

PENERAPAN PENDEKATAN OUTDOOR REAL DRAWING PADA MATA KULIAH GAMBAR ARSITEKTUR UNTUK MENINGKATKAN MINAT DAN MOTIVASI BELAJAR MAHASISWA ARSITEKTUR

Penelitian ini memiliki latar belakang berupa masih kurangnya dasar dasar keahlian (skill) menggambar arsitektur pada mahasiswa arsitektur tingkat pertama, sementara pada tingkat selanjutnya diperlukan kemampuan tersebut. Dengan melalui penelitian ini bertujuan untuk mengetahui dan mamahami permasalahan permasalahan inti pada obyek penelitian tersebut dan kemudian mencari strategi untuk mengembangkan skill dan motivasi mahasiswa dalam hal di atas. Dalam prosesnya pembelajaran terbagi menjadi 2 yakni pembelajaran di kelas dan di luar kelas (outdoor learning). Adapun penelitian ini menggunakan metoda mix method yakni menggabungkan penelitian kuantitatif dan kualitatif, dengan melihat hasil belajar dan persepsi peserta didik dan menganalisisnya guna dapat melakukan refleksi evaluasi dari proses pembelajaran yang telah dilakukan. Dari hasil penelitian didapatkan bahwa terjadi peningkatan hasil belajar yakni menjadi 80% dalam kondisi baik dari hasil pretest awal dimana hanya 15% yang kondisinya baik. Kemudian persepsi mahasiswa yakni pada angka 8,43 pada skala 9 terhadap proses belajar yang telah mereka terima

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Civet coffee, or kopi luwak, has attracted significant attention within the coffee industry in certain regions due to its distinct flavor characteristics that arise from the digestive processes of the civet. The ability to discriminate between wild and feeding civet coffee is of major importance in upholding the industry’s established standards of quality and transparency. This study introduces an innovative method to differentiate between these two coffee types using Headspace Gas Chromatography-Mass Spectrometry (HS-GCMS) with advanced data analysis using machine-learning techniques. This study encompasses seven samples collected from various regions, all of which were subjected to analysis in both roasted and unroasted forms. The data analysis consisted of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), which revealed clear trends that were mostly influenced by processing, indicating how roasting affects the chemical profiles of various coffee types. Further classification was conducted using Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. SVM exhibited notable accuracy at 90%, effectively discriminating between wild and feeding civet coffee, whereas RF outperformed it with a remarkable 100% accuracy. This study contributes to the field of coffee characterization by presenting a robust approach to discriminate between roasted and unroasted wild and feeding civet coffee. This tool serves as a starting step for a valuable resource for both farmers and customers, as it promotes sustainable and ethical practices while retaining the distinct flavor characteristics of this exceptional specialty coffee