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Civet coffee refers to the coffee that includes partlydigested coffee cherries eaten and defecated by the palm civet cat (Paradoxurus hermaphrodites). Civet cats seek out and eat only the ripest, reddish coffee cherries. The coffee cherry fruit is sweet and is completely digested, but the beans are excreted in their feces. It is one of the rarest and priciest coffees in the world [1]. At present, there is still no accepted standard in determining the authenticity of the civet coffee [2]. Due to its high price, the traditional method of collecting feces from the wild civets has given way to a farming method where civets are kept in cages and force-fed with the cherries. Most of the previous studies done in discriminating civet coffee from ordinary coffee beans used a destructive method of sampling, preparation wherein coffee beans were grounded and results can take a very long time. The forte of the NIRS technique lies in its speed [3]. *Author for correspondence NIRS is an analytical technique that uses no chemicals, gives accurate and precise results in minutes or even continuously, and is simple to install, and is safe to use. It is a rapid method for qualitative and quantitative analysis of a very wide range of materials, powders, slurries, and even solid materials and gases. A "rapid" method is defined as a method that will provide accurate and precise results in two minutes or less, including the time for sample preparation, and accessing the sample too, and removing it from the instrument. The NIRS spectrum analysis does not require samples to be grounded because when the light passes through it, the spectra of light can reveal a certain characteristics or features that are unique to the class of that sample.Artificial neural network (ANN) is a computational system that employs parallel processing based on connections and distributional methodology. ANN is similar in operations to a biological neural network [4,5]. It can be trained to learn and once trained it can be used as a very good classifier. Just like the

Cryptography, which involves the use of a cipher, describes a process of encrypting information so that its meaning is hidden and thus, secured from those who do not know how to decrypt the information. Cryptography algorithms come with the various types including the symmetric key algorithms and asymmetric key algorithms. In this paper, the authors applied the most commonly used algorithm, which is the RSA algorithm together with the Chaos system and the basic security device employed in the worldwide organizations which is the Data Encryption Standard (DES) with the objective to make a hybrid data encryption. The advantage of a chaos system which is its unpredictability through the use of multiple keys and the secrecy of the RSA which is based on integer factorization’s difficulty is combined for a more secure and reliable cryptography. The key generation was made more secure by applying the DES schedule to change the keys for encryption. The main strength of the proposed system is the chaotic variable key generator that chages the value of encrypted message whenever a different number of key is used. Using the provided examples the strength of security of the proposed system was tested and demonstrated.

This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the "pixel pair formation" that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively.