scholarly journals Pengembangan Metode Klasifikasi Biji Kopi Sangrai Arabika Gayo dan Robusta Gayo dengan Metode PCA (Principal Component Analysis) Berdasarkan Pengolahannya

2020 ◽  
Vol 4 (4) ◽  
pp. 562-571
Author(s):  
Cut Faradilla Zha Zha Maura ◽  
Rahmat Fadhil ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Roasted Coffee ◽  
Near Infrared Reflectance ◽  
Coffee Beans ◽  
Medium Level ◽  
Pca Method ◽  
Ft Ir

Abstrak. Tanaman kopi merupakan suatu tanaman yang dapat meningkatkan sumber devisa negara lewat ekspor biji mentah maupun olahan dari biji kopi. Pengolahan kopi yang berbeda maka akan menghasilkan mutu kopi yang berbeda juga, semakin bagus prosesnya maka akan semakin tinggi mutu dan harga dari kopi. Pendeteksian perbedaan proses pengolahannya yang cepat dan efisien dapat diwujudkan dengan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Tujuan yang ingin dicapai dalam penelitian ini adalah untuk membangun metode klasifikasi kopi arabika dan robusta Gayo menggunakan pengolahan full wash dan semi wash dalam bentuk biji kopi yang telah disangrai. Kopi disangrai pada tingkat medium (200-205ºC) dalam waktu 16 menit. Akuisisi spektrum kopi menggunakan Self developed FT-IR IPTEK T-1516. Selanjutnya data spektrum diolah menggunakan unscrambler software® X version 10.1 dengan metode PCA (Principal Component Analysis). Hasil penelitian menunjukkan bahwa NIRS dengan metode PCA mampu mengklasifikasikan biji kopi sangrai berdasarkan pengolahannya yaitu Semi wash dan Full wash. Melalui studi ini ditemukan juga selang panjang gelombang yang dapat mengidentifikasikan kualitas kopi sehingga dapat digunakan untuk penelitian selanjutnya dalam pengembangan model identifikasi kualitas kopi.Development of Classification Methods for Gayo Roasted Arabica Coffee and Gayo Robusta by PCA Method (Principal Component Analysis)Abstract. Coffee crop is a plant that can increase the country's foreign exchange source through the export of raw beans and processed coffee beans. Different coffee processing will produce different coffee quality as well, the better the process then the quality and price of the coffee is more higher. Therefore, alternative rapid and efficiently method is needed to detect differences in the processing of coffee. Near Infrared Spectroscopy (NIRS) can be considered to be used due to its advantages. The main objective of this study is to build classification method of Gayo Arabica and Robusta coffee using fullwash and semiwash processing in form of roasted. Coffee is roasted at a medium level (200-205ºC) within 16 minutes. Acquisition of the coffee spectrum using Self-developed FT-IR IPTEK T-1516. Furthermore, the spectrum data is processed using unscrambler software ® X version 10.1 with the PCA (Principal Component Analysis) method. The results showed that NIRS with the PCA method was able to classify roasted coffee beans based on its processing, namely Semi wash and Full wash. Through this study, it was also found that wavelength intervals can identify coffee quality so that it can be used for further research in developing coffee quality identification models

scholarly journals Klasifikasi Cepat dan Non-Destruktif Bubuk Biji Kakao Berdasarkan Tingkat Fermentasi Menggunakan Near Infrared Reflectance Spectroscopy

2016 ◽  
Vol 1 (1) ◽  
pp. 1046-1051
Author(s):  
Rita Zahara ◽  
Agus Arip Munawar ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Method Development ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Accurate Estimate ◽  
Peak Performance ◽  
Cocoa Beans ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak.  Kakao merupakan salah satu komoditas perkebunan andalan di Provinsi Aceh. Hampir keseluruhan areal perkebunan kakao adalah perkebunan rakyat. Biji kakao dari perkebunan rakyat cenderung masih bermutu rendah yang disebabkan oleh pengolahan pascapanen yang kurang baik seperti masalah fermentasi biji kakao. Penjaminan mutu biji kakao melalui pengembangan metode pendugaan mutu yang cepat dan akurat menjadi kata kunci, peningkatan daya saing ekspor biji kakao Indonesia ditingkat dunia. Sampel biji kakao mentah varietas lindak. Sampel dibuat dalam  bentuk bubuk sebanyak 44 sample (10 gr per sampel) dengan penggunaan alat NIRS FT-IR IPTEK T-1516. Klasifikasi data spektrum menggunakan Principal Component Analysis (PCA) dengan tiga  pretreatment spektrum yaitu: de-trending, mean normalization dan standart normal variate. Hasil penelitian diperoleh yaitu Panjang gelombang 1910-2170 nm merupakan, panjang gelombang yang relevan untuk menduga procyanidin pada bubuk biji kakao. Penambahan pretreatment mampu memperbaiki tampilan puncak penanda procyanidin pada spektrum bubuk biji kakao, PCA tanpa pretreatment tidak mampu mengklasifikasi bubuk biji kakao berdasarkan tingkat fermentasi sedangkan dengan bantuan pretreatment mampu mengklasifikasi dengan tingkat keberhasilan diatas 85%, Pretreatment terbaik dalam meningkatkan kinerja PCA dalam klasifikasi bubuk biji kakao berdasarkan tingkat fermentasi yaitu SNV dengan tingkat keberhasilan  97,72 %.Abstract. Cocoa is one Aceh’s most  samples were beans plantation commodities. Most of cocoa belong to the small holder estates. Unfortunately cocoa beans owned by the locals, tend to have low quality as a result of poor postharvest management, such as a cocoa beans fermentation related issue. The assurance of cocoa beans quality through a rapid and accurate estimate method development will be a key in the efforts to promote global export competitions of Indonesia’s cocoa beans. The following sample is raw cocoa beans of lindak variety. Samples were made in the form of cocoa powder with a total of 44 samples (10 gr per samples) using an instrument of NIRS FT-IR IPTEK T-1516. The spectrum data classification uses the Principal Component Analysis  (PCA) three spectrum pretreatment, namely de-trending , mean normalization and standard normal variate. The result show that wavelength range of1910-2170 nm were considered as relevant wavelengths  to predict procyanidin on cocoa seed powder. The addition of the pretreatment will fix procyanidin peak performance on the cocoa beans powder based on the fermentation level of success over 85%. The best pretreatment to increase the PCA permonce classifying the cocoa beans powder according to fermentation level is SNV and the level of success is 97,72%.Keywords: 

scholarly journals Aplikasi NIRS dan Principal Component Analysis (PCA) untuk Mendeteksi Daerah Asal Biji Kopi Arabika (Coffea arabica)

2016 ◽  
Vol 1 (1) ◽  
pp. 954-960
Author(s):  
Syahrul Ramadhan ◽  
Agus Arip Munawar ◽  
Diswandi Nurba
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Coffee Beans

Abstrak. Kopi merupakan spesies tanaman berbentuk pohon yang termasuk dalam famili Rubiaceae dan genus Coffea, tumbuh tegak, bercabang dan bila dibiarkan dapat tumbuh mencapai tinggi 12 meter. Pendeteksian mutu pangan yang cepat dan efisien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Sebanyak 54 sampel biji kopi diambil dari 6 Provinsi yang berbeda, yaitu: Aceh, Bali, Bengkulu, Nusa Tenggara Barat, Jawa Barat dan Jawa Timur. Pengamatan meliputi Principal Component Analysis (PCA) sebagai metode klasifikasi dan Pretreatment Multiplicative Scatter Correction (MSC) sebagai metode koreksi spektrum. Hasil pengujian menunjukkan bahwa PCA hanya mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Jawa Timur, sedangkan dengan penambahan Pretreatment MSC mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Bali dengan tingkat keberhasilan 100%.Abstract. Coffee is belong to family Rubiaceae and the genus Coffea, grow upright, branched, and can grow up to 12 meters high. The detection of food quality quickly and efficiently can be realized through the development of Near Infrared Reflectance Spectroscopy (NIRS) technology. A total of 54 Coffee bean samples were taken from 6 different province, namely: Aceh, Bali, Bengkulu, West Nusa Tenggara, West Java and East Java. Data analysis included Principal Component Analysis (PCA) were used to classify coffee based on geographic origin. Multiplicative Scatter Correction (MSC) method was used as spectra correction. The results shows that PCA is able to classify coffee beans from the Aceh and East Java province, while the addition of MSC Pretreatment able to classify the coffee beans from the province of Aceh and Bali province with 100% success rate.

scholarly journals Aplikasi Teknologi Near Infrared Reflectance Spectroscopy (NIRS) Untuk Membedakan Beras Apek dan Tidak Apek Menggunakan Metode Principal Component Analysis (PCA)

2019 ◽  
Vol 4 (2) ◽  
pp. 387-396
Author(s):  
Herlina Herlina ◽  
Susi Chairani ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Ft Ir

Abstrak. Beras merupakan salah satu tanaman pangan utama hampir dari setengah populasi dunia. Beras sebagai menu pokok ini memiliki kandungan pati yang cukup besar. Selain itu, dalam beras juga mengandung vitamin, protein, mineral, dan air. Pendistribusian beras terkadang membuat beras rusak  yang disebabkan oleh beberapa faktor, seperti penyimpanan yang terlalu lama, dan suhu tempat penyimpanan beras. Beras yang terendam air juga bisa menyebabkan beras itu rusak, seperti beras yang ada dalam gudang yang terkena air hujan yang dapat menyebabkan beras tersebut  bau apek. Tujuan dari penelitian ini adalah membangun model pendugaan mutu beras berdasarkan sifat apek beras menggunakan metode Principal Component Analysis (PCA) dengan pretreatment De-Trending (DT). Penelitian ini menggunakan alat FT-IR IPTEK T-1516. Bahan yang digunakan adalah beras varietas Ciherang 20 g per sampel dengan total jumlah sebanyak 56 sampel. Untuk memperoleh beras apek dilakukan perendaman selama 2 jam dengan penyimpanan 2 hari, 4 hari dan 6 hari dan beras dikeringkan di bawah sinar matahari. Perlakuan terhadap bahan dibagi 2, pertama beras tanpa campuran dan kedua beras dengan campuran. Pencampuran beras bagus dengan beras apek dengan rasio 75% dan 25%. Akuisisi spectrum beras dilakukan dalam bentuk tumpukan. Masing-masing sampel yang telah dimasukkan ke dalam botol plastik akan dilakukan pengambilan spektrum dengan cara diletakkan masing-masing sampel tersebut pada lubang sinar. Untuk mengekplorasi kemiripan spectrum antar sampel dan untuk mencari outlier data dengan menggunakan metode Hotteling T2 ellipse. Hasil dari penelitian yang telah dilakukan diperoleh NIRS mampu menghasilkan klasifikasi beras bagus dan beras apek dengan tingkat keberhasilan di atas 80%. Pretreatment DT mampu menghasilkan model klasifikasi beras sehingga mencapai keberhasilan 83,33%. Technology Application Near Infrared Reflectance Spectroscopy (NIRS) To Distinguish The Rice Is Stale And Not Stale Using The Principal Component Analysis Method (PCA)Abstract. Rice is one of the main food crops of almost half the world's population. Rice as a staple menu has a considerable starch content. In addition, rice also contains vitamins, protein, minerals, and water. The distribution of rice sometimes destroys rice caused by several factors, such as too long storage, and the temperature where the rice is stored. Rice that is submerged in water can also cause the rice to be damaged, such as rice in a warehouse exposed to rain which can cause the rice to smell musty. The purpose of this study is to build a model for estimating the quality of rice based on the musty nature of rice using the Principal Component Analysis (PCA) method with pretreatment De-Trending (DT). This study used the FT-IR tool of Science and Technology T-1516. The material used was rice of Ciherang variety of 20 g per sample with a total amount of 56 samples. To obtain musty rice, soaking is carried out for 2 hours with storage of 2 days, 4 days and 6 days and the rice is dried in the sun. Treatment of ingredients is divided into 2, first rice without mixture and both rice with mixture. Mixing good rice with musty rice with a ratio of 75% and 25%. Acquisition of spectrum of rice is done in the form of piles. Each sample that has been inserted into a plastic bottle will be taken spectrum by placing each of these samples in a ray hole. To explore the similarity spectrum between samples and to find outliers of data using the T2 ellipse Hotteling method. The results of the research that has been done obtained by NIRS are able to produce a classification of good rice and musty rice with a success rate above 80%. DT pretreatment was able to produce a rice classification model so that it achieved 83.33% success.        

scholarly journals Pengembangan Metode Pengujian Keaslian Beras Aceh Menggunakan Nirs Dengan Metode PCA

2019 ◽  
Vol 4 (1) ◽  
pp. 578-587
Author(s):  
Masyitah Masyitah ◽  
Syahrul Syahrul ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan untuk menilai keaslian beras Aceh berdasarkan spektrum NIRS yang dihasilkan. Pendeteksian keaslian beras Aceh secara cepat dan efesien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Penelitian ini menggunakan beras varietas Sigupai (Aceh Barat Daya), varietas  Sanbay (Simeulue) dan varietas Ciherang. Jumlah sampel yang digunakan pada penelitian ini adalah 45 sampel. Pengukuran spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Klasifikasi data spektrum beras menggunakan Principal Component Analysis (PCA) dengan dua  pretreatment yaitu De-trending dan Multiplicative Scatter Correction. Hasil penelitian ini diperoleh yaitu: Spektrum NIRS beras menunjukkan keberadaan kandungan lemak pada panjang gelombang 2355 nm - 2462 nm. Kandungan karbohidrat pada panjang gelombang 2256 nm - 2321 nm.  Kandungan protein pada panjang gelombang 2056 nm - 2166 nm. Kandungan kadar air pada panjang gelombang 1910 nm-1980 nm dan panjag gelombang 1411 nm - 1492 nm menunjukkan kandungan protein dan kadar air. NIRS dengan metode PCA mampu membedakan pencampuran beras Sigupai dengan beras Ciherang dimana pembedaan terbaik terjadi dalam bentuk dua macam pengelompokan yaitu beras  Sigupai ≥ 75 dan beras Sigupai ≤50 dan pretreatment de-trending merupakan pretreatment terbaik dalam mengklasifikasi beras Aceh (Sigupai dan Sanbay) dengan beras Nasional (Ciherang).Development of Methods for Testing the Authenticity of Aceh Rice Using NIRS with the PCA MethodAbstract. The purpose of this study is to develop a prediction model to assess the authenticity of Aceh rice based on the NIRS spectrum produced. The detection of the authenticity of Aceh rice quickly and efficiently can be realized through technological development Near Infrared Reflectance Spectroscopy (NIRS). This study uses Sigupai rice varieties (Aceh Barat Daya), Sanbay (Simeulue) and Ciherang. The number of samples used in this study was 45 samples. Measurement of rice spectrum  using Self developed FT-IR IPTEK T-1516. Rice spectrum data classification uses the Principal Component Analysis (PCA) with two pretreatments, namely De-trending and Multiplicative Scatter Correction. The results of this study were obtained: NIRS spectrum of rice showed the presence of fat content at a wavelength of 2355 nm - 2462 nm. Carbohydrate content at wavelength 2256 nm - 2321 nm. Protein content at wavelength 2056 nm - 2166 nm. The content of water content at a wavelength of 1910 nm-1980 nm and wave length of 1411 nm - 1492 nm shows the protein content and water content. NIRS with the PCA method was able to distinguish the mixing of Sigupai rice from Ciherang rice where the best differentiation occurred in the form of two types of grouping namely Sigupai rice ≥ 75 and Sigupai rice ≤ 50 and de-trending pretreatment was the best pretreatment in classifying Aceh rice (Sigupai and Sanbay) with National rice (Ciherang).

scholarly journals Pengembangan Metode Klasifikasi Biji Kopi Beras Arabika Gayo dan Robusta Gayo dengan Metode PCA (Principal Component Analysis) Berdasarkan Pengolahannya

2020 ◽  
Vol 4 (4) ◽  
pp. 472-481
Author(s):  
Ilka Agusti Febriyansyah ◽  
Rahmat Fadhil ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coffee Bean ◽  
Wet Process ◽  
Robusta Coffee ◽  
Coffee Beans ◽  
Ft Ir ◽  
The Difference ◽  
Spectrum Data

Abstrak. Kopi merupakan salah satu tanaman yang telah banyak dibudidayakan karena memiliki manfaat dan memiliki nilai jual yang cukup tinggi. Pengolahan kopi secara basah dapat dilakukan dengan dua cara yaitu dengan cara basah (full wash)  dan semi basah (semi wash). Secara visual sulit mengidentifikasi perbedaan dari biji kopi beras robusta proses basah (full wash) dengan kopi semi basah (semi wash). Tujuan yang ingin dicapai dalam  penelitian  ini adalah untuk membangun metode klasifikasi kopi Arabika Gayo dan Robusta Gayo dalam bentuk biji kopi beras menggunakan pengolahan basah (full wash) dan pengolahan semi basah (semi wash). Bahan yang digunakan dalam penelitian ini biji kopi beras Arabika dan Robusta dari tanah Gayo. Penelitian ini menggunakan Principal Component Analysis (PCA) sebagai metode pengolah data spektrum. Pengukuran spektrum kopi menggunakan Self developed FT-IR IPTEK T-1516. Panjang gelombang yang digunakan pada penelitian ini antara 1000-2500 nm dengan interval 0.4 nm. Data spektrum diolah menggunakan unscrambler software® X version 10.1. Hasil penelitian menunjukkan bahwa NIRS dengan metode PCA juga mampu mengklasifikasikan biji kopi beras full wash dengan semi wash pada biji kopi Arabika dan Robusta dimana zat dominan pembeda adalah asam amino dan lemak.Development of Gayo Arabica and Robusta Gayo Arabica Coffee Bean Classification Methods with PCA( Principal Component Analysis) Method Based on ProcessingAbstract. Coffee is a plant that has been widely cultivated because it has benefits and has a high selling value. Wet coffee processing can be done in two ways, namely by means of wet (full wash) and semi-wet (semi wash). It is visually difficult to identify the difference between the wet process robusta coffee beans (full wash) and semi-wash coffee. The aim of this research is to develop a method of classifying Arabica Gayo and Robusta Gayo coffee in the form of rice coffee beans using wet wash (full wash) and semi wash. The material used in this study was Arabica and Robusta rice coffee beans from Gayo soil. This study uses Principal Component Analysis (PCA) as a method for processing spectrum data. The measurement of coffee spectrum uses Self-developed FT-IR IPTEK T-1516. Wavelengths used in this study are between 1000-2500 nm with 0.4 nm intervals. Spectrum data are processed using unscrambler software® X version 10.1. The results showed that NIRS with PCA method was also able to classify full wash coffee beans with semi wash in Arabica and Robusta coffee beans where the dominant differentiating substances were amino acids and fats.

Principal Component Analysis of Diffuse Near-Infrared Reflectance Data from Paper Currency

1989 ◽  
Vol 43 (8) ◽  
pp. 1399-1405 ◽  
Author(s):  
John M. Dale ◽  
Leon N. Klatt
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Test System ◽  
Reflectance Spectra ◽  
Sample Population ◽  
Infrared Reflectance ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance

Product tampering and product counterfeiting are increasing the need for methods to quickly determine product authenticity. One of the concepts that we are investigating for the detection of counterfeit objects involves the use of pattern recognition techniques to analyze multivariant data acquired from properties intrinsic to the object. The near-infrared reflectance spectra of currency and other paper stock were used as a test system. The sample population consisted of authentic currency, circulated and uncirculated, and cotton and rag paper stock as stand-ins for counterfeit currency. Reflectance spectra were obtained from a spot that was essentially void of printing on both sides of the currency specimens. Although the reflectance spectra for all of the samples were very similar, principal component analysis separated the samples into distinct classes without there being any prior knowledge of their chemical or physical properties. Class separation was achieved even for currency bills that differed only in their past environment. Leave-One-Out procedures resulted in 100% correct classification of each member of the sample set. A K-Nearest-Neighbor test or a linear discriminate can be used to correctly classify unknown samples.

scholarly journals Principal Component Analysis of Near Infrared Spectra as a Tool of Endosperm Mutant Characterisation and in Barley Breeding for Quality

2011 ◽  
Vol 41 (No. 3) ◽  
pp. 89-95 ◽  
Author(s):  
L. Munck ◽  
B. Møller
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Barley Breeding ◽  
Starch Mutants ◽  
Endosperm Mutant

Near infrared technology, now widespread in quality control, makes it possible to obtain a total multivariate physical chemical fingerprint of the barley endosperm with high precision. Whole spectroscopic fingerprints of the physics and chemistry of barley seeds can be interpreted by multivariate analysis (chemometrics), by Principal Component Analysis (PCA) for classification and Partial Least Squares Regression (PLSR) for correlation. PCA classification of Near Infrared Reflectance (NIR) spectra can differentiate between mutants and alleles in the lys3 and lys5 loci. PCA on NIR can also be used as a routine in barley breeding to select for a multi-gene quality complex in barley as a whole e.g. increasing starch and reducing fibre content. This is done directly from the PCA classification plot by “data breeding” selecting the recombinants which are approaching the position of the normal high starch controls on the plot. Based on classification of NIR spectra, two alleles in the lys5 locus were characterised as a new class of (1→3,1→4)--glucan compensating starch mutants indicating a metabolic connection between starch and -glucan.  

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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