Judul: Pengambangan Kemasan Atmosfir Termodifikasi Pada Penyimpanan Fillet Ikan Mas (Cyprinus carpio) Menggunakan Edible Coating Dari Khitosan
Abstrak: Perkembangan bisnis eceran atau retail di Eropa berakibat pada kenaikan yang cukup tajam akan permintaan produk makanan dengan menggunakan Modified Atmosphere Packaging (MAP). Teknik MAP sekarang digunakan dengan cakupan lebih luas pada makanan segar atau makanan dingin, termasuk daging mentah, daging dan ayam matang, ikan, pasta segar, buah-buahan dan sayuran, dan terakhir sekali adalah pada kopi, teh dan produk-produk roti. Secara umum Modified Atmosphere Packaging (MAP) didefinisikan sebagai teknik pengawetan produk, dimana komposisi udara didalam kemasan dimodifikasi sehingga dapat menghasilkan masa simpan yang lebih lama
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Judul: Pengembangan Teknologi Kemasan Atmosfer Termodifikasi pada Penyimpanan Fillet Ikan Mas Segar dengan Menggunakan Edible Coating dari Karagenan
Abstrak: Pengemasan atmosfer termodifikasi adalali suatu cara perlindungan terhadap produk pangan dengan menggunakan bahan perintang gas (high gas barrier). Kondisi tersebut dapat mengurangi pertumbuhan mikroba dan memperlambat aktivitas enzim pembusuk dengan tujuan memperpanjang umur simpan produk (Young, et al, 1988) Untuk memaksimalkan daya awet ikan segar, maka pengaplikasian teknik MAP dikombinasikan dengan suhu rendah dan edible coating pada produk selama penyimpanan menjadi hal yang menarik untuk dilakukan pengkajian mengenai penentuan konsentrasi gas CO2 optimum dalam kemasan atmosfer termodifikasi untuk penyimpanan fillet ikan segar. Selain itu untuk mengetahui proses kemunduran mutu fillet ikan mas berpelapis karagenan pada suhu dingin (chilling) secara MAP selama penyimpanan
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Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis